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International Conference on Computer and Electrical Engineering 4th (ICCEE 2011)

Jianhong Zhou
Jianhong Zhou
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This work explores Markov-chain structured recurrence relations for generation and perception of chaotic time series. A recurrence relation realized by a multi-layer neural network of radial basis functions (RBF) is employed to characterize the memory-less conditional expectation of a high order Markov process in short time scale. On the basis, a stochastic Markov chain is employed to emulate model switching among multiple recurrence relations embedded within chaotic time series. The high-order recurrence relation is expressed by a nonlinear RBF network derived by Levenberg-Marquardt learning subject to paired data auto-regressively sampling from dynamically segmented chaotic time series. The proposed Markov modeling...

Key Words
1. Introduction
2. Recurrence Relation Approximation
3. Markov-Chain Organized Recurrence Relations
4. Numerical Simulations
5. Conclusions
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