Neural networks: an introduction
Neural networks: an introduction
IBM Systems Journal
Neural network architectures: an introduction
Neural network architectures: an introduction
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Feedforward nets for interpolation and classification
Journal of Computer and System Sciences
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The local minima of the error surface of the 2-2-1 XOR network
Annals of Mathematics and Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Stability analysis of a three-term backpropagation algorithm
Neural Networks
Artificial Neural Networks
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Advances in Artificial Neural Systems
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This paper describes the performance evaluation for the feed forward neural network with three different soft computing techniques to recognition of hand written English alphabets. Evolutionary algorithms for the hybrid neural network are showing the numerous potential in the field of pattern recognition. We have taken five trials and two networks of each of the algorithm: back propagation, Evolutionary algorithm, and Hybrid Evolutionary algorithm respectively. These algorithms have been taken the definite lead on the conventional approaches of neural network for pattern recognition. It has been analyzed that the feed forward neural network by two Evolutionary algorithms makes better generalization accuracy in character recognition problems. The problem of not convergence the weight in conventional backpropagation has also eliminated by using the soft computing techniques. It has been observed that, there are more than one converge weight matrix in character recognition for every training set. The results of the experiments show that the hybrid evolutionary algorithm can solve challenging problem most reliably and efficiently. These algorithms have also been compared on the basis of time and space complexity for the training set.