## Artificial neural networksThis new text has been designed to present the concepts of artificial neural networks in a concise and logical manner for your computer engineering students. |

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### Contents

Artificial Neural Networks | 1 |

A Brief Overview of Neural Computing | 7 |

nection Complexity and Problem Scale 1 6 5 Feedback | 20 |

Copyright | |

15 other sections not shown

### Common terms and phrases

activation function application approach approximation architecture artificial neural networks associative memory backpropagation behavior bias binary biological Chapter clustering concept connections consider constraint convergence corresponding defined defuzzification denoted desired determine elements Equation error example feedforward FF ANN FF network formulation fuzzy sets fuzzy systems genetic algorithms gradient descent Hebbian hidden layer hidden units Hopfield network IEEE IEEE Transactions implementation initial input pattern interconnection iteration Kohonen learning algorithm linear linearly separable mapping matrix membership functions minimization neti Neural Networks neuron nonlinear Note optimization output layer output units overall parameters pattern recognition perceptron problem procedure pseudoinverse RBF unit recall recurrent network representation sample Section self-organizing shown in Figure sigmoid sigmoid function simulation SOFM solution squashing function step strategy TDNN tion topology training algorithm training set unit characteristic unit output unit weights unsupervised learning update values weight correction weight space WLIC yields