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Wang, Youhua ; Ikeda, Kazushi ; Nakayama, Kenji
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Ikeda, Kazushi ; Suzuki, Akihiro ; Nakayama, Kenji
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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
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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.
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Miyoshi, Seiji ; Ikeda, Kazushi ; Nakayama, Kenji
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Kobori, Hideki ; Ikeda, Kazushi ; Nakayama, Kenji
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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
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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.
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Wang, Youhua ; Ikeda, Kazushi ; Nakayama, Kenji
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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 />The numerical property of an adaptive filter algorithm is the most important prob
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lem in practical applications. Most fast adaptive filter algorithms have the numerical instability problem and the fast Newton transversal filter (FNTF) algorithms are no exception. In this paper, we propose a numerically stable fast Newton type adaptive filter algorithm. Two problems are dealt with in the paper. First, we derive the proposed algorithm from the order-update fast least squares (FLS) algorithm. This derivation is direct and simple to understand. Second, we give the stability analysis using a linear time-variant state-space method. The transition matrix of the proposed algorithm is given. The eigenvalues of the ensemble average of the transition matrix are shown to be asymptotically all less than unity. This results in a much improved numerical performance compared with the FNTF algorithms. The computer simulations implemented by using a finite-precision arithmetic have confirmed the validity of our analysis.
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Minamimoto, Kazuhiro ; Ikeda, Kazushi ; Nakayama, Kenji
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In order to analyze the topological structure of the data space using Kohonen's self-organizing feature map (SOFM), a criterion is discussed. The Euclidian distance between the reference vector and the data, the number of the reference
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vectors and the topology preserving measure are taken into account, and are combined in a unified criterion. Through computer simulation, it is confirmed that goodness of the different reference topologies, that is dimensions, can be clearly discriminated regardless the parameters. Thus, the unified criterion makes it possible to analyze the essential data space topology.
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Motoi, Kosuke ; Ikeda, Kazushi ; Kuwae, Y. ; Yuji, T. ; Higashi, Y. ; Nogawa, Masamichi ; Tanaka, Shinobu ; Yamakoshi, Ken-ichi
概要:
山越, 憲一<br />金沢大学ベンチャー・ビジネス・ラボラトリー<br />金沢大学大学院自然科学研究科知的システム創成<br />Monitoring of posture change in sagittal plane and wa
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lking speed is important for evaluate the effectiveness of rehabilitation program or brace. We have developed a wearable device for monitoring human activity. However, in the previous system, there still remain several drawbacks for practical use such as accuracy in angle measurement, cumbersome cable arrangements, and so on. In order to improve these practical drawbacks, a new sensor system was designed, and its availability was evaluated. The results demonstrated that the accuracy of this system showed superior to that of the previous, and this system appears to be a significant means for quantitative assessment of the patient's motion. ©2006 IEEE.
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Ikeda, Kazushi ; Wang, Youhua ; Nakayama, Kenji
概要:
金沢大学理工研究域電子情報学系<br />The numerical property of the recursive least squares (RLS) algorithm has been extensively studied.
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However, very few investigations are reported concerning the numerical behavior of the predictor-based least squares (PLS) algorithms which provide the same least squares solutions as the RLS algorithm. In Ref. [9], we gave a comparative study on the numerical performances of the RLS and the backward PLS (BPLS) algorithms. It was shown that the numerical property of the BPLS algorithm is much superior to that of the RLS algorithm under a finite-precision arithmetic because several main instability sources encountered in the RLS algorithm do not appear in the BPLS algorithm. This paper theoretically shows the stability of the BPLS algorithm by error propagation analysis. Since the time-variant nature of the BPLS algorithm, we prove the stability of the BPLS algorithm by using the method as shown in Ref. [6]. The expectation of the transition matrix in the BPLS algorithm is analyzed and its eigenvalues are shown to have values within the unit circle. Therefore we can say that the BPLS algorithm is numerically stable.
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