An approach to improve Wang-Smith chaotic simulated annealing.
Previous research shows that Wang-Smith chaotic simulated annealing, which employs a gradually decreasing time-step, has only a scaling effect to computational energy of the Hopfield model without changing its shape. This makes the net has sensitive dependence on the value of damping factor. Considering Chen-Aihara chaotic simulated annealing with decaying self-coupling has a shape effect to computational energy of the Hopfield model, a novel approach to improve Wang-Smith chaotic simulated annealing, which reaps the benefits of Wang-Smith model and Chen-Aihara model, is proposed in this paper. With the aid of this method the improved model can affect on computational energy of the Hopfield model from scaling and shape. By adjusting the time-step, the improved neural network can also pass from a chaotic to a non-chaotic state. From numerical simulation experiments, we know that the improved model can escape from local minima more efficiently than original Wang-Smith model.
Zheng
Liying
L
Automatic College, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Wang
Kejun
K
Tian
Kai
K
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics