[1]HU Zhiqiang,LI Wenjing,QIAO Junfei.Frequency-conversion sinusoidal chaotic neural network with disturbance feature[J].CAAI Transactions on Intelligent Systems,2018,13(4):493-499.[doi:10.11992/tis.201703003]
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Frequency-conversion sinusoidal chaotic neural network with disturbance feature

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 493-499 Column: 学术论文—机器学习 Public date: 2018-07-05
Title:
Frequency-conversion sinusoidal chaotic neural network with disturbance feature
Author(s):
HU Zhiqiang1; 2;  LI Wenjing1; 2;  QIAO Junfei1; 2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
Keywords:
disturbance; trigonometric function; wavelet function; chaotic neural network; frequency conversion sinusoidal; combination optimization
CLC:
TP18
DOI:
10.11992/tis.201703003
Abstract:
In this paper, we propose a novel frequency-conversion sinusoidal chaotic neuron model with a disturbance feature to study the anti-disturbance ability of the frequency-conversion sinusoidal chaotic neural network (FCSCNN). To do so, we introduce trigonometric function and wavelet function disturbances into the internal state of the chaotic neuron model. We present a reversed bifurcation diagram of the chaotic neuron and a time evolution diagram of the Lyapunov exponent and then analyze the dynamic properties. We constructed a new transient chaotic neural network (TCNN) using the novel chaotic neuron model. By selecting different disturbance coefficients, we performed network function optimization and combinational optimization. Simulation results show that the FCSCNN can effectively solve function optimization and combinational optimization problems with appropriate disturbance coefficients, which demonstrate the strong robustness and anti-disturbance ability of the model.
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