A Novel Chaotic Neural Network With the Ability to Characterize Local Features and Its Application

作者：Zhao Lin^*; Sun Ming; Cheng JianHua; Xu YaoQun

来源：IEEE Transactions on Neural Networks, 2009, 20(4): 735-742.

DOI：10.1109/TNN.2009.2015943

• Asymptotical stability
• chaotic neural network
• local features
• optimization
• wavelet self-feedback

To provide an ability to characterize local features for the chaotic neural network (CNN), Gauss wavelet is used for the self-feedback of the CNN with the dilation parameter acting as the bifurcation parameter. The exponentially decaying dilation parameter and the chaotically varying translation parameter not only govern the wavelet self-feedback transform but also enable the CNN to generate complex dynamics behavior preventing the network from being trapped in the local minima. Analysis of the energy function of the CNN indicates that the local characterization ability of the proposed CNN is effectively provided by the wavelet self-feedback in the manner of inverse wavelet transform and that the proposed CNN can achieve asymptotical stability. The experimental results on traveling salesman problem (TSP) suggest that the proposed CNN has a higher average success rate for obtaining globally optimal or near-optimal solutions.

• 出版日期2009-4
• 单位哈尔滨工程大学; 哈尔滨商业大学