An expert system based on wavelet decomposition and neural network for modeling Chua's circuit

This paper presents an expert system based on wavelet decomposition and neural network for modeling and simulation of Chua's circuit which is used for chaos studies. The problems which arise in modeling Chua's circuit by neural networks are high structural complexity and slow and difficult training. With this proposed method a new solutions is produced to solve these problems. Wavelet decomposition is used for new useful feature extracting from input signal and neural network is used for modeling. Test results of proposed wavelet decomposition and neural network model are compared with test results of neural network model. Desired performance is provided by this new model. Test results showed that the suggested method can be used efficiently for modeling nonlinear dynamical systems.

[1]  A. L. Baranovski,et al.  Chaotic and random point processes: analysis, design, and applications to switching systems , 2003 .

[2]  Chi-Chuan Hwang,et al.  A linear continuous feedback control of Chua's circuit , 1997 .

[3]  Ned J. Corron,et al.  A new approach to communications using chaotic signals , 1997 .

[4]  Li Wenbo,et al.  A chaos-based robust wavelet-domain watermarking algorithm , 2004 .

[5]  Guanrong Chen,et al.  Suppressing or inducing chaos in a model of robot arms and mechanical manipulators , 2004 .

[6]  Tao Yang,et al.  Application of neural networks to unmasking chaotic secure communication , 1998 .

[7]  Arif Gülten,et al.  Examination of chaotic behaviours using bond graph model , 2003, J. Frankl. Inst..

[8]  Ahmet Arslan,et al.  An expert system for diagnosis of the heart valve diseases , 2002, Expert Syst. Appl..

[9]  M. Marchesi,et al.  Learning of Chua's circuit attractors by locally recurrent neural networks , 2001 .

[10]  Alan V. Oppenheim,et al.  Synchronization of Lorenz-based chaotic circuits with applications to communications , 1993 .

[11]  Michel J. P. Gingras,et al.  Glassiness versus Order in Densely Frustrated Josephson Arrays , 1998 .

[12]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[13]  Michael Peter Kennedy Chaos in the Colpitts oscillator , 1994 .

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  Igor Djurovic,et al.  Time-frequency representations-based detector of chaos in oscillatory circuits , 2006, Signal Process..

[16]  Leon O. Chua,et al.  Chaos from phase-locked loops , 1988, 1988., IEEE International Symposium on Circuits and Systems.

[17]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[18]  Recai Kiliç,et al.  Experimental study on impulsive synchronization between two modified Chua's circuits , 2006 .

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[20]  Thomas Schreiber,et al.  Constrained Randomization of Time Series Data , 1998, chao-dyn/9909042.