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Technical Committee on Nonlinear Problems (NLP) (Searched in: 2007)
Search Results: Keywords 'from:2008-03-27 to:2008-03-27'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2008-03-27 10:30 |
Hyogo | Stability of ILM in 3 period LC Ladder Circuit using Hill's Equation Shouta Ukai, Takashi Hisakado (Kyoto Univ)  NLP2007-153 |
Symmetric three-phase circuit is a basic model of electric power systems.Single phase mode and two phase mode in which ... [more] |
NLP2007-153 pp.1-6 |
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| NLP | 2008-03-27 10:55 |
Hyogo | Synchronization Phenomena in Oscillators Coupled by Switching Resistors with Timing Mismatch Yoko Uwate, Yoshifumi Nishio (Tokushima Univ.) NLP2007-154 |
Synchronization phenomena in coupled oscillatory systems are very important model to describe various higher-dimensional... [more] | NLP2007-154 pp.7-12 |
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| NLP | 2008-03-27 11:20 |
Hyogo | Bifurcation Phenomena and Braess's Paradox in Selfish Routing Takurou Misaka, Toshimitsu Ushio, Takafumi Kanazawa, Yasuhiko Fukumoto (Osaka Univ.) NLP2007-155 |
Braess's paradox is caused by selfish routing on the computer networks. To resolve the paradox, methods of adding latenc... [more] | NLP2007-155 pp.13-16 |
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| NLP | 2008-03-27 11:45 |
Hyogo | * Hironori Kumeno, Yoshifumi Nishio (Tokushima Univ.) NLP2007-156 |
In this study, we investigate synchronization of parametrically excited van der Pol oscillators. By carrying out compu... [more] |
NLP2007-156 pp.17-20 |
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| NLP | 2008-03-27 13:10 |
Hyogo | Prediction of Periods on Chaos Time Series
-- Dependence on Precision of Chaos Neural Network Outputs -- Takeshi Murakami (Iwate Univ.), Satoshi Kawamura (Ishinomaki Senshu Univ.), Hitoaki Yoshida (Iwate Univ.) NLP2007-157 |
We have developed chaos neural networks(CNN) and applied to a cryptosystem. This work focuses on a period of determinist... [more] | NLP2007-157 pp.21-26 |
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| NLP | 2008-03-27 13:35 |
Hyogo | Analysis of a Gene by the Use of Chaos having L=4 Jiguo Dong (uec), Takako Yamada (KG), Katsufusa Shono NLP2007-158 |
Chaos having $L=4$, where Lyapunov exponent is $\lambda={\it ln}L$ and $L=4$, can be generated by calculating twice the ... [more] | NLP2007-158 pp.27-32 |
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| NLP | 2008-03-27 14:00 |
Hyogo | Chaotic Map with Chaotically Changing Parameter for a Cryptosystem Shuichi Aono, Yoshifumi Nishio (Tokushima Univ.) NLP2007-159 |
In this research, we propose a two-dimensional chaotic map with a chaotically changing parameter for a cryptosystem. And... [more] | NLP2007-159 pp.33-36 |
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| NLP | 2008-03-27 14:25 |
Hyogo | New developments in large deviation statistics and time correlation calculations in chaotic dynamics and stochastic processes Syuji Miyazaki, Miki Kobayashi, Kei Ejima, Mika Izuo, Taro Takaguchi, Kai Morino (Kyoto Univ.) NLP2007-160 |
First, spectra statistics of transition matrices of the Watts-Strogatz model are studied. Different statistics are obs... [more] |
NLP2007-160 pp.37-42 |
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| NLP | 2008-03-27 15:00 |
Hyogo | Analysis method of chaotic time series using measures of networks. Yutaka Shimada, Tohru Ikeguchi (Saitama Univ.) NLP2007-161 |
Complex phenomena are ubiquitous in the real world, for example, fluctuation of financial indices in a stock market, po... [more] |
NLP2007-161 pp.43-48 |
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| NLP | 2008-03-27 15:25 |
Hyogo | Noise-free Resonance in Chaotic Neural Systems Naofumi Katada, Sou Nobukawa, Haruhiko Nishimura (Univ. of Hyogo) NLP2007-162 |
Stochastic resonance (SR) is known as a phenomenon in which the presence of noise helps a nonlinear system in amplifying... [more] | NLP2007-162 pp.49-54 |
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| NLP | 2008-03-27 15:50 |
Hyogo | Stabilizing Unknown Steady State of Discrete-time Chaotic Systems Tadashi Tsubone, Kenichi Kurimoto, Yasuhiro Wada (Nagaoka Univ. of Tech.) NLP2007-163 |
An control method with dynamic state for stabilizing unknown unstable fixed points of nonlinear systems is introduced. T... [more] | NLP2007-163 pp.55-60 |
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| NLP | 2008-03-27 16:25 |
Hyogo | An Analysis of Weight Enumerating Function of Convolutional Codes using Non-linear Difference Equations Hideki Yoshikawa (Tohoku Gakuin Univ.) NLP2007-164 |
[more] | NLP2007-164 pp.61-64 |
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| NLP | 2008-03-27 16:50 |
Hyogo | Generating Sparse Matrices for Power/Signal Integrity Evaluation Yuichi Tanji (Kagawa Univ.), Hideki Asai (Shizuoka Univ.) NLP2007-165 |
[more] | NLP2007-165 pp.65-70 |
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| NLP | 2008-03-27 17:15 |
Hyogo | An Efficient Algorithm for Finding All DC Solutions of Piecewise-Linear Circuits Kiyotaka Yamamura, Mitsuru Tonokura, Wataru Takahashi (Chuo Univ.) NLP2007-166 |
An efficient algorithm is proposed for finding all DC solutions of transistor circuits where characteristics of transist... [more] | NLP2007-166 pp.71-76 |
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| NLP | 2008-03-27 17:40 |
Hyogo | Convergence of Iterative Refinement for Ill-conditioned Linear Systems Shin'ichi Oishi (Waseda.Univ.) |
[more] | ||
| NLP | 2008-03-28 10:00 |
Hyogo | Applications of Cellular Neural Networks with Mixture Template Takashi Inoue, Masaru Nakano, Yoshifumi Nishio (Tokushima Univ.) NLP2007-167 |
In this research, we propose cellular neural networks using mixture template as an example of space-varying cellular n... [more] |
NLP2007-167 pp.1-6 |
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| NLP | 2008-03-28 10:25 |
Hyogo | Lazy Self-Organizing Map for Effective Self-Organization Taku Haraguchi, Haruna Matsushita, Yoshifumi Nishio (Tokushima Univ.) NLP2007-168 |
The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo ... [more] | NLP2007-168 pp.7-12 |
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| NLP | 2008-03-28 10:50 |
Hyogo | A multi-agent Reinforcement Learning with Parallel Computation and its Application to Chaos Control Norihisa Sato, Masaharu Adachi (Tokyo Denki Univ.) NLP2007-169 |
A reinforcement learning is a trial and error process, therefore it takes a huge computation time. It multiple agents ca... [more] | NLP2007-169 pp.13-18 |
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| NLP | 2008-03-28 11:15 |
Hyogo | Action Learning by An Artificial Neural Network Risa Shimada, Kenya Jin'no (KGUniv.) NLP2007-170 |
We would like to understand a learning feature of human. To realize such purpose, we try to learn an action by artificia... [more] | NLP2007-170 pp.19-24 |
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| NLP | 2008-03-28 11:40 |
Hyogo | A Note on Expansion and Contraction of Categories in Fuzzy ARTMAP Takeshi Kamio (Hiroshima City Univ.), Kunihiko Mitsubori (Takushoku Univ.), Chang-Jun Ahn, Hisato Fujisaka, Kazuhisa Haeiwa (Hiroshima City Univ.) NLP2007-171 |
Fuzzy ARTMAP (FAM) is a supervised learning system. FAM segments the input space with categories which are defined by hy... [more] | NLP2007-171 pp.25-30 |
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