Title :
Chaotic neural networks and their applications
Author :
He, Yuyao ; Wang, Lipo
Author_Institution :
Coll. of Marine Eng., Northwestern Polytech. Univ., Xian, China
Abstract :
Many difficult combinatorial optimization problems arising from science and technology are often difficult to solve exactly. Hence a great number of approximate algorithms for solving combinatorial optimization problems have been developed (Reeves, 1993, Zanakis et al., 1989). Hopfield and Tank (1985) applied the continuous-time, continuous-output Hopfield neural network (CTCO-HNN) to TSP, thereby initiating a new approach to optimization problems. But the Hopfield neural network is often trapped in local minima because of its gradient descent property. A number of modifications have been done on Hopfield neural networks for escaping from local minima. So far, incorporating chaos into the Hopfield neural network has been moved to be a successful approach to improving the convergent property of the HNNs. In this paper, we first review three chaotic neural network models, and then propose a novel approach to chaotic simulated annealing. Second, we apply all of them to a 10 city TSP. The time evolutions of energy functions and outputs of neurons for each model are given. The features and effectiveness of four methods are discussed and evaluated according to the simulation results. We conclude that the proposed neural network with simulated annealing has more powerful ability to obtain global minima than any other chaotic neural network model when applied to difficult combinatorial optimization problems
Keywords :
chaos; neural nets; simulated annealing; travelling salesman problems; 10 city TSP; approximate algorithms; chaotic neural networks; chaotic simulated annealing; combinatorial optimization problems; continuous-time continuous-output Hopfield neural network; convergent property; energy functions; global minima; gradient descent property; local minima; time evolutions; Biological system modeling; Cellular neural networks; Chaos; Educational institutions; Helium; Hopfield neural networks; Marine technology; Neural networks; Neurons; Simulated annealing;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863345