Novel wavelet chaotic neural network for solving optimization problems

doi:10.3778/j.issn.1002-8331.2010.11.013

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (11): 42-45.DOI: 10.3778/j.issn.1002-8331.2010.11.013

• 研究、探讨 • Previous Articles Next Articles

Novel wavelet chaotic neural network for solving optimization problems

SUN Ming¹，ZHAO Lin¹，XU Yao-qun²，DING Ji-cheng¹

1.College of Automation，Harbin Engineering University，Harbin 150001，China
2.Institute of System Engineering，Harbin Commerce University，Harbin 150028，China

Received:2008-10-09 Revised:2009-03-03 Online:2010-04-11 Published:2010-04-11
Contact: SUN Ming

一种求解优化问题的新型小波混沌神经网络

孙明¹，赵琳¹，徐耀群²，丁继成¹

1.哈尔滨工程大学自动化学院，哈尔滨 150001
2.哈尔滨商业大学自动化学院，哈尔滨 150028

通讯作者: 孙明

Abstract

Abstract: Based on the phenomena that the neuron’s self-feedback can generate chaos，Gauss wavelet function is used as the self-feedback of chaotic neuron in this paper.The effect of the scale and shift parameters in Gauss wavelet on the neuron dynamics is analyzed and the chaotic neuron with double simulated annealing mechanisms realized respectively by the self-feedback connection weight and the Gauss wavelet scale is proposed.The chaotic neural network is constructed and the effect of the additional energy function generated by Gauss wavelet function on the optimization ability of the network is studied.The simulation results on optimization problems show that the proposed network can find the global optimum of the optimization problems with a faster speed.

Key words: Gauss wavelet, chaotic neural network, optimization problems

摘要： 基于神经元的自反馈项可产生混沌的现象，将Gauss小波函数作为混沌神经元的自反馈项。分析了Gauss小波的尺度和平移参数对神经元动力学的影响，提出了自反馈连接权和Gauss小波尺度双重模拟退火的混沌神经元。构建了混沌神经网络模型，分析了由Gauss小波函数产生的附加能量函数对网络优化能力的影响。优化问题的仿真结果表明，该网络能够以较快的速度找到优化问题的全局最优解。

关键词: Gauss小波, 混沌神经网络, 优化问题

CLC Number:

TP393

SUN Ming¹，ZHAO Lin¹，XU Yao-qun²，DING Ji-cheng¹. Novel wavelet chaotic neural network for solving optimization problems[J]. Computer Engineering and Applications, 2010, 46(11): 42-45.

孙明¹，赵琳¹，徐耀群²，丁继成¹. 一种求解优化问题的新型小波混沌神经网络[J]. 计算机工程与应用, 2010, 46(11): 42-45.

[1]	YU Xin, WU Lingzhen, WANG Yanlin. New Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems [J]. Computer Engineering and Applications, 2019, 55(12): 37-43.
[2]	XU Xinlin, HU Zhongbo, HE Xianping, SU Qinghua. Improved backtracking search optimization algorithm with self-adaptable mutation scale factor and hybrid selection strategy [J]. Computer Engineering and Applications, 2017, 53(17): 6-13.
[3]	YE Yonggang1, XU Yaoqun2. SLF chaotic neural network model and its application [J]. Computer Engineering and Applications, 2015, 51(21): 74-79.
[4]	WANG Kairong, RAN Hui. Non-parameter filled function for unconstrained optimization problems [J]. Computer Engineering and Applications, 2014, 50(14): 64-67.
[5]	GONG Guobin. Adaptive constrained optimization hybrid Particle Swarm Optimization algorithm [J]. Computer Engineering and Applications, 2013, 49(9): 50-53.
[6]	WU Huawei, CHEN Tefang. Central force constrained optimization algorithm with dynamic hierarchical and engineering application [J]. Computer Engineering and Applications, 2013, 49(15): 14-18.
[7]	LONG Wen1, LIANG Ximing, JIAO Jianjun. Hybrid algorithm for solving constrained optimization problems [J]. Computer Engineering and Applications, 2012, 48(9): 9-11.
[8]	LONG Wen1, XU Songjin2, JIAO Jianjun1. Improved constrained optimization differential evolution algorithm [J]. Computer Engineering and Applications, 2012, 48(25): 34-38.
[9]	LONG Wen. Improved multi-objective constrained optimization differential evolution algorithm [J]. Computer Engineering and Applications, 2012, 48(21): 5-8.
[10]	LI Wenjing, LIU Zhijia, LAN Zhenxiong. BFGS relaxation asynchronous parallel algorithm of unconstrained optimization problems [J]. Computer Engineering and Applications, 2012, 48(17): 44-47.
[11]	YE Cheng-xu¹，LI He-cheng^2，3. Genetic algorithm for a class of nonconvex optimization problems [J]. Computer Engineering and Applications, 2009, 45(24): 60-62.
[12]	XU Yao-qun^1,2,YUE Hai-yan². Research on simulated annealing parameters in wavelet chaotic neural network [J]. Computer Engineering and Applications, 2008, 44(2): 80-82.
[13]	JianMei Xiao. Artificial Fish-Swarm Algorithm for Solving Constrained Optimization Problems [J]. Computer Engineering and Applications, 2007, 43(3期): 40-40.
[14]	HE Yang-lin,YE Chun-ming. Research on Cultural Evolutionary Algorithms applied to scheduling optimization problems [J]. Computer Engineering and Applications, 2007, 43(36): 55-57.
[15]	. A novel mixed-strategy evolutionary programming algorithm [J]. Computer Engineering and Applications, 2007, 43(13): 89-91.

Novel wavelet chaotic neural network for solving optimization problems

一种求解优化问题的新型小波混沌神经网络

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics