A Genetic Learning Algorithm for Generating a Parsimonious Functional Link Network

Status	已發表Published
	A Genetic Learning Algorithm for Generating a Parsimonious Functional Link Network
	Chen C.L.P.; Bhumireddy C.
	2003-12-01
Source Publication	Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
Pages	179-184
Abstract	A genetic learning algorithm is proposed for supervised learning of a parsimonious functional link networks (FLN), where Gaussian functions are used in the functional nodes. The parameters to be adjusted using genetic approach are weights between input layer and functional nodes, and parameters, i.e., center and width, of Gaussian functions (radial basis functions) in the functional nodes. Genetic coding is used for combining evolution of weights and Gaussian parameters in a single chromosome. Singular Value Decomposition (SVD) is used for computing the weights in the output layer. The proposed approach is very effective in terms of computational efficiency and time complexity as demonstrated with several benchmark datasets. The simulations indicate that proposed approach yields consistent results, which is superior to previous approaches.
Keyword	Functional link networks Genetic algorithms Machine learning Parsimonious networks
URL	View the original
Language	英語English
Fulltext Access	View Full-Text via Scopus
Document Type	Conference paper
Collection	University of Macau
Affiliation	University of Texas at San Antonio
Recommended Citation GB/T 7714	Chen C.L.P.,Bhumireddy C.. A Genetic Learning Algorithm for Generating a Parsimonious Functional Link Network[C], 2003, 179-184.
APA	Chen C.L.P.., & Bhumireddy C. (2003). A Genetic Learning Algorithm for Generating a Parsimonious Functional Link Network. Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence, 179-184.

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