This paper presents a method for designing neural networks using a genetic algorithm (GA) with deterministic mutation (DM) based on learning. The GA presented in this paper has a large framework including DM, which is performed on the basis of the results from neural network learning. It can achieve
Integration of supervised ART-based neural networks with a hybrid genetic algorithm
โ Scribed by Shing Chiang Tan; Chee Peng Lim
- Publisher
- Springer
- Year
- 2010
- Tongue
- English
- Weight
- 616 KB
- Volume
- 15
- Category
- Article
- ISSN
- 1432-7643
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
In spite of great importance of fuzzy feed-forward and recurrent neural networks (FNN) for solving wide range of real-world problems, today there is no e ective learning algorithm for FNN. In this paper we propose an e ective geneticbased learning mechanism for FNN with fuzzy inputs, fuzzy weights e
The stock market, which has been investigated by various researchers, is a rather complicated environment. Most research only concerned the technical indexes (quantitative factors), instead of qualitative factors, e.g., political e ect. However, the latter plays a critical role in the stock market e