Construction of fuzzy systems using least-squares method and genetic algorithm
Fuzzy Sets and Systems - Theme: Modeling and control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Analysis and evaluation in a welding process applying a Redesigned Radial Basis Function
Expert Systems with Applications: An International Journal
International Journal of Wireless and Mobile Computing
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A well-performing set of radial basis functions (RBFs) can emerge from genetic competition among individual RBFs. Genetic selection of the individual RBFs is based on credit sharing which localizes competition within orthogonal niches. These orthogonal niches are derived using singular value decomposition and are used to apportion credit for the overall performance of the RBF network among individual nonorthogonal RBFs. Niche-based credit apportionment facilitates competition to fill each niche and hence to cover the training data. The resulting genetic algorithm yields RBF networks with better prediction performance on the Mackey-Glass chaotic time series than RBF networks produced by the orthogonal least squares method and by k-means clustering