Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On using back propagation neural networks to separate single echoes from multiple echoes
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
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the artificial neural network training algorithm is implemented in MATLAB language. This implementation is focused on the network parameters in order to get the optimal architecture of the network that means (the optimal neural network is the network that can reach the goals in minimum number of training iterations and minimum time of training). Many examples were tested and it was shown that using one hidden layer with number of neuron equal to the square of the number of inputs will lead to optimal neural network by mean of reducing the number of training stages (number of training iterations) and thus the processing time needed to train the network.