Algorithms for better representation and faster learning in radial basis function networks
Advances in neural information processing systems 2
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks
A genetic procedure for RBF neural networks centers selection
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Genetic Algorithm Based Clustering: A Survey
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Decision fusion method to improve the performances of multispectral ATR systems
SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
A new version of the flusser invariants set for pattern feature extraction
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
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The efficiency of pattern recognition (PR) systems using RBF neural networks to implement their recognition function, depends a lot by the training algorithms of these neural networks and especially, by the specific techniques (e.g., supervised, clustering techniques etc.) used for RBF center positioning. Having as starting point the basic property of genetic algorithms (GA) to represent global searching tools, a full-genetic approach to assure optimization both connectivity and neural weights of RBF networks is proposed. In order to confirm the broached theoretical aspects and based on real pattern recognition task, a comparative study (as performance level) with others standard RBF training methods and SART neural network is also indicated.