Neural Networks
ISITC '07 Proceedings of the 2007 International Symposium on Information Technology Convergence
Expert Systems with Applications: An International Journal
Advances in Engineering Software
Application of Probabilistic Neural Network Model in Evaluation of Water Quality
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01
ICEE '10 Proceedings of the 2010 International Conference on E-Business and E-Government
Rainfall Prediction Using Generalized Regression Neural Network: Case Study Zhengzhou
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
IEEE Transactions on Information Theory
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
IEEE Transactions on Neural Networks
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
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In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.