000			01490nam a2200205Ia 4500
999			_c1179 _d1179
003			OSt
005			20200224124231.0
008			140801s2009 xx 000 0 und d
020			_a9788120313514
082			_a006.32 AND-I
100			_aAnderson, James A
245			_aAN INTRODUCTION TO NEURAL NETWORKS
260			_bPHI Pub. _c2009 _aNew Delhi
300			_a650p.
500			_aIntroduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Representation of Information. Applications of Simple Associators: Concepts Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann Machines. Nearest Neighbor Models. Adaptive maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network
650			_aProperties of Single Neurons , Synaptic Integration and Neuron Models
700			_aJames A ANDERSON
856			_uhttps://books.google.co.in/books?id=_ib4vPdB76gC&printsec=frontcover&dq=an+introduction+to+neural+networks+by+anderson&hl=en&sa=X&ved=0ahUKEwjD-ZbD__7fAhXKR30KHYiUCI4Q6AEIKDAA#v=onepage&q=an%20introduction%20to%20neural%20networks%20by%20anderson&f=false
942			_2ddc _cBK