Ten lectures on wavelets
Applying evolutionary programming to selected traveling salesman problems
Cybernetics and Systems
Neural Networks - 2005 Special issue: IJCNN 2005
Activation Function of Wavelet Chaotic Neural Networks
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Improved transiently chaotic neural network and its application to optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Wavelet chaotic neural networks and their application to optimization problems
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Critical temperature of the transiently chaotic neural network
Mathematical and Computer Modelling: An International Journal
On chaotic simulated annealing
IEEE Transactions on Neural Networks
A unified framework for chaotic neural-network approaches to combinatorial optimization
IEEE Transactions on Neural Networks
Focused local learning with wavelet neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A class of chaotic neural network with morlet wavelet function self-feedback
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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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.