Name
Abstract | ||
|---|---|---|
This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1142/S1469026808002375 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Genetic algorithm, neural network, pattern recognition, sunspot forecasting | Biological neuron model,Computer science,Stochastic neural network,Probabilistic neural network,Time delay neural network,Artificial intelligence,Artificial neural network,Genetic algorithm,Machine learning,Network structure | Journal |
Volume | Issue | ISSN |
7 | 4 | 1469-0268 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. H. Frank Leung | 1 | 183 | 16.00 |
| S. H. Ling | 2 | 609 | 40.29 |
| H. K. Lam | 3 | 3618 | 193.15 |