Name
Abstract | ||
|---|---|---|
In this paper, Fourier series chaotic neural network model is presented to improve the ability to escape the local minima so that it can effectively solve optimization problems 10-city traveling salesman problem was given and the effects of the non-monotonous degree in the model on solving 10-city traveling salesman problem were discussed, the figures of the reversed bifurcation and the maximal Lyapunov exponents of single neural unit were given The new model is applied to solve several function optimizations Seen from the simulation results, the new model is powerful than the common chaotic neural network. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13278-0_18 | ISNN (1) |
Keywords | Field | DocType |
non-monotonous degree,maximal lyapunov exponent,salesman problem,single neural unit,chaotic neural network model,new model,optimization problem,common chaotic neural network,local minimum,fourier series | Trigonometric functions,Computer science,Maxima and minima,Travelling salesman problem,Fourier series,Artificial intelligence,Chaotic neural network,Optimization problem,Machine learning,Lyapunov exponent,Bifurcation | Conference |
Volume | ISSN | ISBN |
6063 | 0302-9743 | 3-642-13277-4 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-hai Zhang | 1 | 0 | 1.35 |
| Chen-zhi Sun | 2 | 0 | 0.34 |
| Yao-qun Xu | 3 | 67 | 11.98 |