Introduction To The Theory Of Neural ComputationCRC Press, 8 бер. 2018 р. - 352 стор. Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest. |
Зміст
Introduction | 1 |
The Hopfield Model | 11 |
Extensions of the Hopfield Model | 43 |
Optimization Problems | 71 |
Simple Perceptrons | 89 |
MultiLayer Networks | 115 |
Recurrent Networks | 163 |
Unsupervised Hebbian Learning | 197 |
Unsupervised Competitive Learning | 217 |
Formal Statistical Mechanics of Neural Networks | 251 |
APPENDIX Statistical Mechanics | 275 |
Bibliography | 281 |
| 307 | |
| 321 | |
Інші видання - Показати все
Introduction To The Theory Of Neural Computation John A. Hertz,Anders S. Krogh,Richard G. Palmer Обмежений попередній перегляд - 2018 |
Introduction To The Theory Of Neural Computation, Volume I John A Hertz,Anders Krogh,Richard G Palmer Перегляд фрагмента - 1991 |
Introduction To The Theory Of Neural Computation, Volume I John A Hertz,Anders Krogh,Richard G Palmer Перегляд фрагмента - 1991 |
Загальні терміни та фрази
algorithm applied approach appropriate architecture attractor average back-propagation binary bits Boltzmann machine calculate Chapter competitive learning connection strengths context units continuous-valued convergence cost function defined discussed dynamics eigenvalues eigenvector energy function equations equilibrium error example feature mapping feed-forward feed-forward networks FIGURE finite gives gradient descent Grossberg Hebb rule Hebbian learning hidden layer hidden units Hopfield network IEEE implementation input patterns input space input vector Kohonen learning rule linear linearly magnetic matrix mean field memory minimize neural networks neurons nonlinear Oja's optimization output layer output units parameters particular perceptron Perror Physics possible principal component probability problem random receptive fields recurrent network result Section sequence shown in Fig shows signal simple perceptron solution solved spin spin glass stable statistical mechanics stochastic symmetric tanh temperature term theory tion training set Travelling Salesman Problem unsupervised learning update values weight space weight vector zero