1.

論文

論文
Nakayama, Kenji
出版情報: IEEE transactions on circuits and systems.  31  pp.1002-1008,  1984-12-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3949
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />A simultaneous frequency- and time-domain approximation method for discrete-time filters is proposed. In the method, transfer function coefficients are divided into two subsets, X//1 and X//2, which are employed for optimizing a time response and a frequency response, respectively. Frequency and time responses are optimized through the iterative Chebyshev approximation method and a method of solving linear equations, respectively. At the rth iteration step, the maximum frequency response error, which appeared at the (r minus 1)th step, is minimized, and X//2**(**r** minus **1**) becomes X//2**(**r**). X//1**(**r**) is obtained from linear equations including X//2**(**r**) as a constant. The frequency response at the rth step is evaluated using the above obtained X//1**(**r**) and X//2**(**r**). This means the optimum time response is always guaranteed in the frequency-response approximation procedure. A design example of a symmetrical impulse response shows the new approach is more efficient than conventional methods from the filter order reduction viewpoint. 続きを見る
2.

論文

論文
Hara, Kazuyuki ; Nakayama, Kenji
出版情報: IEICE Trans. Fundamentals.  E81-A  pp.374-381,  1998-03-01. 
URL: http://hdl.handle.net/2297/5654
概要: 金沢大学大学院自然科学研究科知能情報・数理<br />A training data selection method is proposed for multilayer neural networks (MLNNs). This met hod selects a small number of the training data, which guarantee both generalization and fast training of the MLNNs applied to pattern classification. The generalization will be satisfied using the data locate close to the boundary of the pattern classes. However, if these data are only used in the training, convergence is slow. This phenomenon is analyzed in this paper. Therefore, in the proposed method, the MLNN is first trained using some number of the data, which are randomly selected (Step 1). The data, for which the output error is relatively large, are selected. Furthermore, they are paired with the nearest data belong to the different class. The newly selected data are further paired with the nearest data. Finally, pairs of the data, which locate close to the boundary, can be found. Using these pairs of the data, the MLNNs are further trained (Step 2). Since, there are some variations to combine Steps 1 and 2, the proposed method can be applied to both off-line and on-line training. The proposed method can reduce the number of the training data, at the same time, can hasten the training. Usefulness is confirmed through computer simulation. 続きを見る
3.

論文

論文
Nakayama, Kenji ; Kato, Takuo ; Katayama, Hiroshi
出版情報: IEEE&INNS Proc. IJCNN'93, Nagoya.  pp.2480-2483,  1993-10-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6822
4.

論文

論文
Nakayama, Kenji
出版情報: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  pp.1754-1757,  1998-04-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6858
概要: A bandlimited signal extrapolation algorithm is proposed. J. A. Cadzow's algorithm (1979) is modified to eliminate undesired outband spectra. An inverse filter stopband response is relaxed by adding small random numbers. A constrained heuristic optimization is suggested that can use arbitrary signal properties as constraints. Through numerical examples, it is shown how regularization techniques can improve SNR (signal-to-noise-ratio) by 20 dB. 続きを見る
5.

論文

論文
Keeni, Kanad ; Shimodaira, Hiroshi ; Nakayama, Kenji
出版情報: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR.  2  pp.600-603,  1997-08-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6795
概要: This paper presents an automatic coding scheme for representing the output layer of a neural network. Compared to local representation where the number of output unit is p, the number of output unit required for the proposed representation is close to log p. The output of seven different printers were used for evaluating the performance of the system. The proposed automatic representation gave the average recognition rate of 98.7% for 71 categories. 続きを見る
6.

論文

論文
Wang, Youhua ; Ikeda, Kazushi ; Nakayama, Kenji
出版情報: IEEE Proc. of ICASSP'98, Seattle.  pp.1713-1716,  1998-05-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6833
7.

論文

論文
Nakayama, Kenji ; Katayama, Hiroshi
出版情報: IEEE & INNS Proc. IJCNN'92, Baltimore.  Ⅰ  pp.888-893,  1992-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6846
8.

論文

論文
Ikeda, Kazushi ; Suzuki, Akihiro ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  pp.1896-1900,  1997-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6784
概要: The effects of the quantization of the parameters of a learning machine are discussed. The learning coefficient should be as small as possible for a better estimate of parameters. On the other hand, when the parameters are quantized, it should be relatively larger in order to avoid the paralysis of learning originated from the quantization. How to choose the learning coefficient is given in this paper from the statistical point of view. 続きを見る
9.

論文

論文
Nakayama, Kenji ; Imai, Kunihiko
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  6  pp.3909-3914,  1994-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6836
概要: 金沢大学理工研究域電子情報学系<br />A neural demodulator is proposed for amplitude shift keying (ASK) signal. It has several important features compared with conventional linear methods. First, necessary functions for ASK demodulation, including wide-band noise rejection, pule waveform shaping, and decoding, can be embodied in a single neural network. This means these functions are not separately designed but unified in a learning and organizing process. Second, these functions can be self-organized through the learning. Supervised learning algorithms, such as the backpropagation algorithm, can be applied for this purpose. Finally, both wide-band noise rejection and a very sharp waveform response can be simultaneously achieved. It is very difficult to be done by linear filtering. Computer simulation demonstrates efficiency of the proposed method. 続きを見る
10.

論文

論文
Miyoshi, Seiji ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  3  pp.1913-1918,  1997-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6848
概要: In this paper, the geometric learning algorithm (GLA) is proposed for an elementary perceptron which includes a single output neuron. The GLA is a modified version of the affine projection algorithm (APA) for adaptive filters. The weights update vector is determined geometrically towards the intersection of the k hyperplanes which are perpendicular to patterns to be classified. k is the order of the GLA. In the case of the APA, the target of the coefficients update is a single point which corresponds to the best identification of the unknown system. On the other hand, in the case of the GLA, the target of the weights update is an area, in which all the given patterns are classified correctly. Thus, their convergence conditions are different. In this paper, the convergence condition of the 1st order GLA for 2 patterns is theoretically derived. The new concept `the angle of the solution area' is introduced. The computer simulation results support that this new concept is a good estimation of the convergence properties. 続きを見る
11.

論文

論文
Ma, Zhiqiang ; Nakayama, Kenji ; Yamamoto, G.
出版情報: Proc. IEEE ISCAS'92, San Diego.  pp.1203-1206,  1992-05-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6792
12.

論文

論文
Nakayama, Kenji ; Kuriki, Satoshi
出版情報: Proceedings - IEEE International Symposium on Circuits and Systems.  pp.1483-1486,  1985-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6837
概要: A simplified digital tuned-circuit extractor is proposed. An A/D converter and multipliers are not required. An A/D converter and multipliers are not required. The tuning frequency is determined only by a master clock. The tank circuit output noise is sufficiently suppressed by newly introduced error canceller. Although sampling frequency for the tank circuit is relatively low, the phase of the output signal is effectively detected. A computer simulation shows that phase adjusting for data sampling clock is stable. The proposed timing extractor is easily realized on digital integrated circuits. 続きを見る
13.

論文

論文
Nakayama, Kenji
出版情報: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  3  pp.1197-1200,  1981-04-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6791
概要: A new difference coefficient FIR digital filter is proposed. Its coefficient is obtained as the difference between the successive values of the original coefficients permuted in large magnitude sequence. It is effectively applied to every kind of filter responses. Computation complexity becomes about 18% and 13% for 99th and 299th order FIR filters, compared with direct FIR filter realization. 続きを見る
14.

論文

論文
Miyoshi, Seiji ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  2  pp.1291-1296,  1996-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6790
概要: 金沢大学理工研究域電子情報学系<br />In this paper, probabilistic memory capacity of recurrent neural networks(RNNs) is investigated. Th is probabilistic capacity is determined uniquely if the network architecture and the number of patterns to be memorized are fixed. It is independent from a learning method and the network dynamics. It provides the upper bound of the memory capacity by any learning algorithms in memorizing random patterns. It is assumed that the network consists of N units, which take two states. Thus, the total number of patterns is the Nth power of 2. The probabilities are obtained by discriminations whether the connection weights, which can store random M patterns at equilibrium states, exist or not. A theoretical way for this purpose is derived, and actual calculation is executed by the Monte Carlo method. The probabilistic memory capacity is very important in applying the RNNs to real fields, and in evaluating goodness of learning algorithms. As an example of a learning algorithm, the improved error correction learning is investigated, and its convergence probabilities are compared with the upper bound. A linear programming method can be effectively applied to this numerical analysis. 続きを見る
15.

論文

論文
Nakayama, Kenji ; Ikehara, Keisuke
出版情報: IEEE&INNS, Proc. IJCNN'92, Beijing.  pp.Ⅱ53-Ⅱ58,  1992-11-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6819
16.

論文

論文
Nakayama, Kenji ; Kimura, Yoshinori ; Katayama, Hiroshi
出版情報: Proceedings of the International Joint Conference on Neural Networks.  2  pp.1247-1250,  1993-10-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6789
概要: In this paper, quantization level increase in human face images using a multilayer neural network (NN) is investigated. Basically speaking, it is impossible to increase quality without any other information. However, when images are limited to some category, image restoration could be possible, based on the common properties in this category. The multilayer NN is trained using human face images of 32テ・2 pixels with 8-levels as the input data, and 256-level images as the targets. The standard back-propagation (BP) algorithm is employed. 20, 40 and 100 training data are examined. By increasing the training data, a general function of regenerating missing information can be achieved. The internal structure of the trained NN is analyzed using some special input images. As a result, it has been confirmed that the NN regards the input image as the human face, and extracts features of the face. The input image is transformed using these features and the common properties of the training data, extracted and held on the connection weights, to the human face image. 続きを見る
17.

論文

論文
Kuraishi, Yoshiaki ; Makabe, Takayoshi ; Nakayama, Kenji
出版情報: IEEE Journal of Solid-State Circuits.  17  pp.1039-1044,  1982-12-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3951
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />This paper presents a fully integrated analog front-end LSI chip which is an inte rface system between digital signal processors and existing analog telecommunication networks. The developed analog LSI chip includes many high level function blocks such as A/D and D/A converters with 11 bit resolution, various kinds of SCF's, an agc circuit, an external control level adjuster, a carrier detector, and a zero crossing detector. Design techniques employed are mainly directed toward circuit size reductions. The LSI chip is fabricated in a 5 mu m line double polysilicon gate NMOS process. Chip size is 7. 14 multiplied by 6. 51 mm. The circuit operates on plus or minus 5 v power supplies. Typical power consumption is 270 mw. By using this analog front-end LSI chip and a digital signal processor, modern systems can be successfully constructed in a compact size. 続きを見る
18.

論文

論文
Xu, Q. ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  3  pp.1954-1959,  1997-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6813
概要: This paper investigates some possible problems of Cascade Correlation algorithm, one of which is the zigzag output mapping caused by weight-illgrowth of the adding hidden unit. Without doubt, it could lead to deteriorate the generalization, especially for regression problems. To solve this problem, we combine Cascade Correlation algorithm with regularization theory. In addition, some new regularization terms are proposed in light of special cascade structure. Simulation has shown that regularization indeed smooth the zigzag out-put, so that the generalization is improved, especially for functional approximation. 続きを見る
19.

論文

論文
Nakayama, Kenji ; Chigawa, Yasuhide ; Hasegawa, Osamu
出版情報: IEEE&INNS Proc. IJCNN'92, Baltimore.  Ⅳ  pp.235-240,  1992-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6799
20.

論文

論文
Nakayama, Kenji ; Kato, Takuo
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  pp.4237-4242,  1994-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6821
21.

論文

論文
Yukawa, Akira ; Maruta, Rikio ; Nakayama, Kenji
出版情報: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  pp.1400-1403,  1985-03-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6817
22.

論文

論文
Nakayama, Kenji ; Iwata, Atsushi ; Yanagisawa, Takeshi
出版情報: IEICE transactions on fundamentals of electronics, communications and computer sciences.  E71-E  pp.1177-1188,  1998-12-01. 
URL: http://hdl.handle.net/2297/5643
概要: 金沢大学大学院自然科学研究科情報システム
23.

論文

論文
Hibino, Masao ; Nakayama, Kenji ; Mizukami, Toshihiko
出版情報: Proc. IEEE ISCAS'79, Tokyo.  pp.368-369,  1979-07-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6803
24.

論文

論文
Ohnishi, K. ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  pp.1933-1937,  1996-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6835
概要: 金沢大学理工研究域電子情報学系<br />A neural demodulator is proposed for quadrature amplitude modulation (QAM) signals. It has several important features compared with conventional linear methods. First, necessary functions for the QAM demodulation, including wide-band noise rejection, pulse waveform shaping, and decoding, can be embedded in a single neural network. This means that these functions are not separately designed but are unified in a learning process. Second, these functions can be self-organized through the learning. Supervised learning algorithms, such as the back-propagation algorithm, can be applied for this purpose. Finally, both wide-band noise rejection and a very sharp waveform response can be simultaneously achieved. It is very difficult to be done by linear filtering. Computer simulation demonstrates efficiency of the proposed method. 続きを見る
25.

論文

論文
Hara, Kazuyuki ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  1  pp.436-441,  1996-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6788
概要: 金沢大学理工研究域電子情報学系<br />A training data reduction method for a multilayer neural network (MLNN) is proposed in this paper. This method reduce the data by selecting the minimum number of training data that guarantee generality of the MLNN. For this purpose, two methods are used. One of them is a pairing method which selects the training data by finding the nearest data of the different classes. Data along the class boundary in data space can be selected. The other method is a training method, which used a semi-optimum MLNN in a training process. Since the MLNN classify data based on the distance from the network boundary, the selected data can locate close to the class boundary. So, if the semi-optimum MLNN did not select data from class boundary, pairing method can select them. The proposed methods can be applied to both off-line training and on-line training. The proposed method is also investigated through computer simulation. 続きを見る
26.

論文

論文
Hara, Kazuyuki ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  1  pp.600-605,  1995-11-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6787
概要: Signal classification performance using multilayer neural network (MLNN) and the conventional signal processing methods are theoretically compared under the limited observation period and computational load. The signals with N samples are classified based on frequency components. The comparison is carried out based on degree of freedom the signal detection regions in an N-dimensional signal space. As a result, the MLNN has higher degree of freedom, and can provide more flexible performance for classifying the signals than the conventional methods. This analysis is further investigated throught computer simulations. Multi-frequency signals and the real application, a dial tone receiver, are taken into account. As a result, the MLNN can provide much higher accuracy than the conventional signal processing methods. 続きを見る
27.

論文

論文
Nakayama, Kenji ; Sato, Yayoi ; Kuraishi, Yoshiaki
出版情報: IEEE transactions on circuits and systems.  CAS-32  pp.759-766,  1985-08-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3948
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />Design techniques are described for a switched capacitor adaptive line equalizer which is applied to high-speed (200 kb/s) digital transmission over analog subscriber loops. An equalizer transfer function is approximated so as to minimize intersymbol interference of an isolated pulse response. Optimum pole-zero location, which is suited to line characteristics in a wide frequency band, is also discussed. In order to attain high accuracy capacitor ratios using a small unit capacitor, capacitor values are rounded off into equivalent integer values, and are discretely optimized using pole-zero deviation as an error criterion. The equalizer has a finite number of frequency responses which correspond to line lengths. Gain and delay time differences between the adjoining step responses are compressed. The switched capacitor line equalizer was fabricated using 3- mu m CMOS technology. Measured data were very close to designed performances. 続きを見る
28.

論文

論文
Nakayama, Kenji ; Mizukami, Toshihiko
出版情報: IEEE transactions on circuits and systems.  29  pp.23-24,  1982-01-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3955
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />A new infinite impulse response (IIR) Nyquist filter with zero intersymbol interf erence is proposed. The necessary and sufficient conditions for the transfer function are obtained. The proposed IIR Nyquist filter requires only frequency-domain optimization. Multistep optimization, using the iterative Chebyshev approximation, is proposed. This method is able to design a new kind of IIR Nyquist filter with the minimum order. Numerical examples for 30- and 15-percent rolloff rates are illustrated. From these examples, it is confirmed that the IIR approach can reduce the filter order and hardware size, compared with the conventional finite impulse response (FIR) Nyquist filters. Its efficiency becomes marked for high Q Nyquist filters. 続きを見る
29.

論文

論文
Nakayama, Kenji
出版情報: IEICE transactions on fundamentals of electronics, communications and computer sciences.  E70  pp.735-743,  1987-08-01. 
URL: http://hdl.handle.net/2297/5644
概要: 金沢大学大学院自然科学研究科情報システム<br />This paper proposes a new discrete optimization method which is mainly directed toward saving computing time for high-order FIR filters. A transfer functions first approximated in a cascade form of a low-order function W(z) with pre-rounded coefficients and a high-order function F(z) with infinite precision coefficients. Rounded F(z) coefficients are discretely optimized to minimize the mean square error of the amplitude response. The roundoff error spectrum is shaped to be suppressed by a weighting function W(z). In order to save computing time, the error is equivalently evaluated in a time domain, and the F(z) coefficients are divided into small groups in the discrete optimization procedure. Design examples for 200 tap FIR filters demonstrate practical usefulness. 続きを見る
30.

論文

論文
Miyoshi, Seiji ; Ikeda, Kazushi ; Nakayama, Kenji
出版情報: IEEE&INNS Proc. of IJCNN'98, Anchorage.  pp.2340-2345,  1998-05-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6807
31.

論文

論文
Nakayama, Kenji
出版情報: Proc.IEEE ICASSP'82, Paris.  pp.484-487,  1982-01-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6851
32.

論文

論文
Hirano, Akihiro ; Nakayama, Kenji ; Watanabe, Kazunobu
出版情報: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  2  pp.861-864,  1999-03-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6809
概要: This paper presents convergence characteristics of stereophonic echo cancellers with pre-processing. The convergence analysis of the averaged tap-weights show that the convergence characteristics depends on the relation between the impulse response in the far-end room and the changes of the pre-processing filters. Examining the uniqueness of the solution in the frequency domain leads us to the same relation. Computer simulation results show the validity of these analyses. 続きを見る
33.

論文

論文
Nakayama, Kenji ; Ohsugi, M.
出版情報: IEEE&INNS Proc. of IJCNN'98, Anchorage.  pp.2253-2257,  1998-05-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6828
34.

論文

論文
Nakayama, Kenji ; Mitsutani, N.
出版情報: Proc. IEEE&INNS IJCNN'91, Seattle.  II  pp.A-914-,  1991-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6820
概要: Summary form only given. A novel adaptive threshold method is proposed for an associative memory using a mutually connec ted neural network. Computer simulation was carried out, using 51 and 153 patterns, which appear on a keyboard. The neural network has 16 × 16 = 256 units and full connections. Results demonstrate that dramatic improvements in memory capacity and association rates can be achieved. For example, an association rate for 51 patterns with 40 noises has been increased from 12.2% to 97.7%, compared with a single threshold method. 続きを見る
35.

論文

論文
Nakayama, Kenji ; Mizukami, T.
出版情報: Proceedings - IEEE International Symposium on Circuits and Systems.  pp.317-320,  1985-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6834
概要: A new approach for low-bit-rate coding is proposed. Numerical evaluation for the bit-rate compression ratio and SNR shows the proposed method provides good performance compared with conventional linear PCM coding. Furthermore, since the predictor in the coder does not utilize any of the input signal statistics, this method can be applied for both speech and music coding. 続きを見る
36.

論文

論文
Hara, Kazuyuki ; Nakayama, Kenji
出版情報: IEEE&INNS Proc. IJCNN'93, Nagoya.  1  pp.601-604,  1993-10-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6811
概要: Frequency analysis capability of multilayer neural networks, trained by back-propagation (BP) algorithm is investigated. Multi-frequency signal classification is taken into account for this purpose. The number of frequency sets, that is signal groups, is 2approx.5, and the number of frequencies included in a signal group is 3approx.5. The frequencies are alternately located among the signal groups. Through computer simulation, it has been confirmed that the neural network has very high resolution. Classification rates are about 99.5% for training signals, and 99.0% for untraining signals. The results are compared with conventional methods, including Euclidean distance with accuracy of about 65%, Fourier transform with accuracy of about 10approx.30%, and using very high-Q filters with a huge number of computations. The neural network requires only the same number of inner products as the hidden units. Frequency sensitivity and robustness for the random noise are studied. The networks show high frequency sensitivity, namely, the networks have high frequency resolution. Random noise are added to the multi-frequency signals to investigate how does the network cancel uncorrelated noise among the signals. By increasing the number of samples, or training signals, effects of random noise can be cancelled. 続きを見る
37.

論文

論文
Khalaf, Ashraf A.M. ; Nakayama, Kenji
出版情報: IEEE&INNS Proc. IJCNN'99, Washington,DC.  pp.1590-1593,  1999-07-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6845
38.

論文

論文
Nakayama, Kenji ; Takahashi, Yutaka ; Sato, Yayoi ; Nukada, Yasuaki
出版情報: IEEE Trans. Circuits and Systems.  135  pp.1073-1081,  1988-09-01.  IEEE
URL: http://hdl.handle.net/2297/3944
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />An adaptive switched-capacitor (SC) line equalizer system that can be applied to four-wire full-duplex and multirate digital transmission is described. Several kinds of noises can exist in programmable high-gain SC equalizers, such as switching noise, DC offset jump, and transient response. To avoid their effects, an adaptive SC filter is proposed. Two identical SC circuits are used in parallel. An input signal is fed into both SC circuits, and output signals from both circuits are alternately sent after the above noises are eliminated. Furthermore, many different data rates can be handled by slightly modifying an equalizer circuit. A bridged-tap echo canceller and a DC offset canceller are modified so as to be applied to the proposed adaptive SC filter. A line equalizer system, that handles data rates ranging from 3.2 to 64 kb/s was designed. An LSI was fabricated using a 3-μm CMOS process. Experimental results show that noise effects are mostly eliminated, and frequency responses and eye openings are close to the designed values. 続きを見る
39.

論文

論文
Khalaf, Ashraf A.M. ; Nakayama, Kenji
出版情報: IEICE transactions on fundamentals of electronics, communications and computer sciences.  E81-A  pp.364-373,  1998-03-01. 
URL: http://hdl.handle.net/2297/5646
概要: 金沢大学大学院自然科学研究科情報システム<br />Time series prediction is very important technology in a wide variety of fields. The actual ti me series contains both linear and nonlinear properties. The amplitude of the time series to be predicted is usually continuous value. For these reasons, we combine nonlinear and linear predictors in a cascade form. The nonlinear prediction problem is reduced to a pattern classification. A set of the past samples x(n - 1), . . . , x(n - N) is transformed into the output, which is the prediction of the next coming sample x(n). So, we employ a multi-layer neural network with a sigmoidal hidden layer and a single linear output neuron for the nonlinear prediction. It is called a Nonlinear Sub-Predictor (NSP). The NSP is trained by the supervised learning algorithm using the sample x(n) as a target. However, it is rather difficult to generate the continuous amplitude and to predict linear property. So, we employ a linear predictor after the NSP. An FIR filter is used for this purpose, which is called a Linear Sub-Predictor (LSP). The LSP is trained by the supervised learning algorithm using also i(n) as a target. In order to estimate the minimum size of the proposed predictor, we analyze the nonlinearity of the time series of interest. The prediction is equal to mapping a set of past samples to the next coming sample. The multi-layer neural network is good for this kind of pattern mapping. Still, difficult mappings may exist when several sets of very similar patterns are mapped onto very different samples. The degree of difficulty of the mapping is closely related to the nonlinearity. The necessary number of the past samples used for prediction is determined by this nonlinearity. The difficult mapping requires a large number of the past samples. Computer simulations using the sunspot data and the artificially generated discrete amplitude data have demonstrated the efficiency of the proposed predictor and the nonlinearity analysis. 続きを見る
40.

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Miyoshi, Seiji ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  4  pp.1955-1960,  1995-11-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6830
概要: A recurrent neural network (RNN), in which each unit has serial delay elements, is proposed for memorizing limit cycles (LCs). This network is called DRNN in this paper. An LC consists of several basic patterns. The hysteresis information of LCs, realized on the connections from the delay elements to the units, is very efficient in the following reasons. First, the same basic patterns can be shared by different LCs. This make it possible to drastically increase the number of LCs, even though using a small number of the basic patterns. Second, noise performance, that is, probability of recalling the exact LC starting from the noisy LC, can be improved. The hysteresis information consists of two components, the order of the basic patterns included in an LC, and the cross-correlation among all the basic patterns. The former is highly dependent on the number of LCs, and the latter the number of all the basic patterns. In order to achieve good noise performance, a small number of the basic patterns is preferred. These properties of the DRNN are theoretically analyzed and confirmed through computer simulations. It is also confirmed that the DRNN is superior to the RNN without delay elements for memorizing LCs. 続きを見る
41.

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Ma, Zhiqiang ; Ikeda, Tetsuya ; Nakayama, Kenji
出版情報: Proceeding IEEE ISPACS'96.  pp.1063-1067,  1996-11-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6808
42.

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Nakayama, Kenji ; Inomata, Satoru ; Takeuchi, Yukou
出版情報: IJCNN. International Joint Conference on Neural Networks.  pp.587-592,  1990-06-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/6889
概要: 金沢大学大学院自然科学研究科情報システム<br />A design methodology is presented for digital multilayer neural networks with limited binary e xpressions. An error back-propagation algorithm is modified as follows: the numbers of binary bits used for connections and unit outputs are decreased step by step in the training process. In order to express unit outputs with two-level values, the differential of the logistic function is replaced by a small positive constant used in weight change equations. After the training is completed, binary expressions for connections and unit outputs can be reduced to several-bit and two-level values, respectively. Therefore, no multipliers or nonlinear functions are required in the resulting network, which will be used for pattern recognition. Furthermore, memory capacity and adder circuit hardware can be reduced. The network performance is also insensitive to noisy patterns. 続きを見る
43.

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Kobori, Hideki ; Ikeda, Kazushi ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  pp.804-809,  1996-06-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6839
概要: 金沢大学理工研究域電子情報学系<br />A model of dynamic associative memories is proposed in this paper. The aim is to find all stored pa tterns, and to distinguish the stored and the spurious patterns. Aihara used chaotic neurons and showed that his model has a nonperiodic associative dynamics. In his model, however, it is difficult to distinguish the stored patterns from the others, because the state of the network changes continually. We propose such a new model of neurons that each neuron changes its output to the other when the accumulation of its internal state exceeds a certain threshold. By computer experiments, we show that the state of the network stays at the stored pattern for a while and then travels around to another pattern, and so on. Furthermore, when the number of the stored patterns is small, the stored and the spurious patterns can be distinguished using interval of the network staying these patterns. 続きを見る
44.

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Nakayama, Kenji
出版情報: IEEE Transactions on Acoustics, Speech, and Signal Processing.  36  pp.290-292,  1988-02-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3945
概要: 金沢大学金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />An improved FFT (fast Fourier transform) algorithm combining both decimations in frequency and in time is presented. Stress is placed on a derivation of general formulas for submatrices and multiplicands. Computational efficiency is briefly discussed. 続きを見る
45.

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Nakayama, Kenji
出版情報: IEEE transactions on circuits and systems.  29  pp.404-408,  1982-06-01.  Institute of Electrical and Electronics Engineers (IEEE)
URL: http://hdl.handle.net/2297/3953
概要: 金沢大学大学院自然科学研究科情報システム<br />金沢大学工学部<br />A new approach to the implmentation of sum of products (SP) and transposed sum of products (TSP) using partitioned arithmetic is proposed. This aproach makes it possible to realize SP and TSP with recent advanced semiconductor memory technology without multipliers. It is also shown to offer signficant reductions in memory capacity requirement and computational complexity. Dynamic range constraints and output roundoff noise analysis are also discussed. 続きを見る
46.

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Nakayama, Kenji ; Nishimura, Katsuaki ; Katayama, Hiroshi
出版情報: Proceedings of the International Joint Conference on Neural Mateworks.  3  pp.2603-2606,  1993-10-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6797
概要: Authors have proposed an asymmetrical associative neural network (NN) using variable hysteresis threshold and its learning and association algorithms. It can drastically improve noise performance, that is insensitivity to noise. In this paper, memory capacity bound and threshold optimization in this associative NN are further discussed. Binary random patterns are considered. First, relation between the number of patterns and the number of iterations is investigated. The latter gradually increases until some number of patterns. After that, it suddenly increases. This is a very peculiar phenomenon. This turning point gives the memory capacity bound, that is about 1.56N, where N is the number of units. Next, threshold optimization is discussed. Relation between threshold and noise performance, and effects of connection weight distribution on noise performance are theoretically discussed. Based on these results, a ratio of step-size and the threshold is optimized to be 0.5/(NP-1), where NP is the number of units on the pattern. Numerically statistical simulation demonstrates efficiency of the proposed methods. 続きを見る
47.

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Khalaf, Ashraf A.M. ; Nakayama, Kenji
出版情報: IEEE International Conference on Neural Networks - Conference Proceedings.  3  pp.1975-1980,  1998-05-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6783
概要: Time series prediction is a very important technology in a wide variety of field. The actual time series contains both linear and nonlinear properties. The amplitude of the time series to be predicted is usually continuous value. For this reason, we combine nonlinear and linear predictors in a cascade form. In order to estimate the minimum size of the proposed predictor, we propose a nonlinearity analysis for the time series of interest. Computer simulations using the sunspot data have demonstrated the efficiency of the proposed predictor and the nonlinearity analysis. 続きを見る
48.

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論文
Nakayama, Kenji ; Ma, Zhiqiang ; Yamamoto, G.
出版情報: Proc. IEEE 32nd Midwest Symposium on Circuits and Systems, Illinois.  pp.968-972,  1989-08-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6840
概要: A new pole-zero pairing and ordering strategy which can provide the minimum total capacitance in cascaded biquad switched-capacitor (SC) filters is proposed. Design examples for several kinds of SC filters are demonstrated. The proposed conditions can limit pairing and ordering assignments, by which the total capacitance is concentrated within 5% over the minimum value. 続きを見る
49.

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Ma, Zhiqiang ; Nakayama, Kenji ; Sugiyama, Akihiko
出版情報: IEICE transactions on fundamentals of electronics, communications and computer sciences.  E76-B  pp.751-754,  1993-07-01. 
URL: http://hdl.handle.net/2297/5642
概要: 金沢大学大学院自然科学研究科情報システム<br />An automatic tap assignment method in subband adaptive filter is proposed in this letter. The number of taps of the adaptive filter in each band is controlled by the mean-squared error. The numbers of taps increase in the bands which have large errors, while they decrease in the bands having small errors, until residual errors in all the bands become the same. In this way, the number of taps in a band is roughly proportional to the length of the impulse response of the unknown system in this band. The convergence rate and the residual error are improved, in comparison with existing uniform tap assignment. Effectiveness of the proposed method has been confirmed through computer simulation. 続きを見る
50.

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Nakayama, Kenji ; Seki, T.
出版情報: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.  pp.11.8.1-11.8.4,  1984-04-01.  IEEE(Institute of Electrical and Electronics Engineers)
URL: http://hdl.handle.net/2297/6852