DocumentCode :
2844671
Title :
A novel chaotic neural network with anti-trigonometric function self-feedback
Author :
Xu, Yaoqun ; Yang, Xueling
Author_Institution :
Inst. of Syst. Eng., Harbin Univ. of Commerce, Harbin, China
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
3098
Lastpage :
3103
Abstract :
A Chaotic neural network model with anti-trigonometric function self-feedback is proposed by introducing anti-trigonometric function into self-feedback of chaotic neural network The analyses of the optimization mechanism of the networks suggest that anti-trigonometric function self-feedback affects the original Hopfield energy function in the manner of the sum of the multiplications of anti-trigonometric function to the state, avoiding the network being trapped into the local minima. The energy function is constructed, and the sufficient condition for the networks to reach asymptotical stability is analyzed and is used to instruct the parameter set of the networks for solving traveling salesman problem (TSP). Simulation research on function optimization and TSP indicates that the proposed networks can find the optimal solution of combinatorial optimization problems.
Keywords :
Hopfield neural nets; asymptotic stability; feedback; nonlinear systems; travelling salesman problems; Hopfield energy function; TSP; antitrigonometric function self-feedback; asymptotic stability; chaotic neural network; combinatorial optimization problems; optimization mechanism; traveling salesman problem; Asymptotic stability; Business; Cellular neural networks; Chaos; Damping; Hopfield neural networks; Neural networks; Neurons; Stability analysis; Systems engineering and theory; Anti-trigonometric function self-feedback; Asymptotical stability; Chaotic neural network; Energy function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
Type :
conf
DOI :
10.1109/CCDC.2010.5498642
Filename :
5498642
Link To Document :
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