NDLI: Ridgelet Probabilistic Neural Network with Genetic Algorithm selecting center vectors

Content Provider	IEEE Xplore Digital Library
Author	Fengli Sun Quanhua Gao Jinguo Wang
Copyright Year	2011
Description	Author affiliation: School of Science, Chang'an University, Xi'an, China, 710064 (Quanhua Gao; Jinguo Wang) \|\| School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China, 710072 (Fengli Sun)
Abstract	To enhance the generalization performance of conventional Probabilistic Neural Network, a novel model of Probabilistic Neural Network is proposed in this paper. Ridgelet basis functions satisfying admissible condition are adopted as the activation functions in radial basis layer of the model. To reduce the size of the network and determine the parameters of ridgelet functions including the directions, locations and scales, Genetic Algorithm is utilized to select the optimal central vectors of pattern layer. Then, the normalized central vectors are taken as the direction parameters, the location parameters are all set to 1, and the scales are obtained in the training process of the network or defaulted to the same values. The classification experiments indicate that the new model has smaller size and better classification performance compared with conventional classifiers, especially in aspect of generalization performance.
Starting Page	1330
Ending Page	1335
File Size	5898032
Page Count	6
File Format	PDF
ISBN	9781457715860
e-ISBN	9781457715877
DOI	10.1109/ICCSNT.2011.6182206
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Probabilistic neural network Neurons Genetic algorithm Estimation Probabilistic logic Vectors Center selection Particle swarm optimization Ridgelet Genetic algorithms
Content Type	Text
Resource Type	Article

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