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Genetic Algorithm Based K-Means Fast Learning Artificial Neural Network
| Content Provider | CiteSeerX |
|---|---|
| Author | Xiang, Yin Tay, Alex Phuan, Leng |
| Abstract | a small neural network bearing two types of parameters, the tolerance, δ and the vigilance, µ. In previous papers, it was shown that the KFLANN was capable of fast and accurate assimilation of data [12]. However, it was still an unsolved issue to determine the suitable values for δ and µ in [12]. This paper continues to follows-up by introducing Genetic Algorithms as a possible solution for searching through the parameter space to effectively and efficiently extract suitable values to δ and µ. It is also able to determine significant factors that help achieve accurate clustering. Experimental results are presented to illustrate the hybrid GA-KFLANN ability using available test data. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Parameter Space Significant Factor Available Test Data Previous Paper Extract Suitable Value Accurate Assimilation Small Neural Network Suitable Value Accurate Clustering Unsolved Issue Hybrid Ga-kflann Ability |
| Content Type | Text |
| Resource Type | Article |
