会议专题

Ridgelet Probabilistic Neural Network with Genetic Algorithm Selecting Center Vectors

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.

ridgelet probabilistic neural network center selection genetic algorithm particle swarm optimization

Fengli Sun Quanhua Gao Jinguo Wang

School of Electronics and Information Northwestern Polytechnical University Xian, China, 710072 School of Science Changan University Xian, China, 710064

国际会议

2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

哈尔滨

英文

1330-1335

2011-12-24(万方平台首次上网日期,不代表论文的发表时间)