A novel chaotic neural network with the ability to characterize local features and its application
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Algorithm analysis and application based on chaotic neural network for cellular channel assignment
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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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As an attempt to provide an organized way to study the chaotic structures and their effects in solving combinatorial optimization with chaotic neural networks (CNN), a unifying framework is proposed to serve as a basis where the existing CNN models ran be placed and compared. The key of this proposed framework is the introduction of an extra energy term into the computational energy of the Hopfield model, which takes on different forms for different CNN models, and modifies the original Hopfield energy landscape in various manners. Three CNN models, namely the Chen and Aihara model with self-feedback chaotic simulated annealing [CSA] (1995, 1997), the Wang and Smith model with timestep CSA (1998), and the chaotic noise model, are chosen as examples to show how they can be classified and compared within the proposed framework