Name
Abstract | ||
|---|---|---|
This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (CA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved CA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved CA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1142/S0129065705000438 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
fuzzy logic, genetic algorithm, neural-fuzzy network, neural network | Feedforward neural network,Biological neuron model,Computer science,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Time delay neural network,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
15 | 6 | 0129-0657 |
Citations | PageRank | References |
2 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. H. Ling | 1 | 609 | 40.29 |
| F. H. Frank Leung | 2 | 183 | 16.00 |
| H. K. Lam | 3 | 3618 | 193.15 |