Neural Networks: An IntroductionNeural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers. |
Contents
I | 3 |
II | 9 |
III | 13 |
IV | 18 |
VI | 21 |
VII | 23 |
VIII | 24 |
IX | 26 |
LXVIII | 153 |
LXIX | 155 |
LXX | 159 |
LXXI | 162 |
LXXII | 165 |
LXXIII | 166 |
LXXIV | 167 |
LXXV | 169 |
X | 29 |
XI | 32 |
XII | 34 |
XIII | 38 |
XIV | 40 |
XV | 42 |
XVI | 46 |
XVII | 47 |
XVIII | 48 |
XIX | 49 |
XX | 52 |
XXI | 53 |
XXII | 56 |
XXIII | 60 |
XXIV | 63 |
XXVII | 64 |
XXVIII | 65 |
XXIX | 67 |
XXX | 69 |
XXXI | 70 |
XXXII | 72 |
XXXIII | 74 |
XXXV | 78 |
XXXVI | 79 |
XXXVII | 80 |
XL | 83 |
XLI | 84 |
XLII | 85 |
XLIII | 87 |
XLIV | 93 |
XLV | 95 |
XLVI | 96 |
XLVII | 98 |
XLVIII | 102 |
XLIX | 105 |
L | 108 |
LI | 109 |
LII | 111 |
LIII | 114 |
LIV | 117 |
LV | 119 |
LVI | 122 |
LVII | 124 |
LVIII | 126 |
LIX | 128 |
LX | 130 |
LXI | 133 |
LXII | 135 |
LXIII | 138 |
LXIV | 144 |
LXV | 145 |
LXVI | 149 |
LXVII | 151 |
LXXVI | 171 |
LXXVII | 174 |
LXXVIII | 176 |
LXXIX | 180 |
LXXX | 181 |
LXXXI | 183 |
LXXXII | 185 |
LXXXIII | 191 |
LXXXIV | 194 |
LXXXV | 196 |
LXXXVI | 201 |
LXXXVIII | 205 |
LXXXIX | 209 |
XC | 211 |
XCI | 215 |
XCII | 219 |
XCIII | 223 |
XCIV | 226 |
XCV | 227 |
XCVI | 228 |
XCVII | 231 |
XCVIII | 234 |
XCIX | 239 |
C | 249 |
CI | 251 |
CII | 253 |
CIII | 255 |
CIV | 259 |
CV | 264 |
CVI | 265 |
CVII | 266 |
CVIII | 268 |
CIX | 270 |
CX | 272 |
CXI | 275 |
CXII | 277 |
CXIII | 279 |
CXIV | 281 |
CXV | 283 |
CXVI | 284 |
CXVII | 286 |
CXVIII | 287 |
CXIX | 289 |
CXX | 291 |
CXXI | 292 |
CXXII | 294 |
CXXIII | 296 |
CXXIV | 297 |
CXXV | 300 |
CXXVI | 303 |
CXXVIII | 307 |
325 | |
Other editions - View all
Neural Networks: An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickland Limited preview - 2012 |
Neural Networks: An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickland No preview available - 2014 |
Common terms and phrases
activation architecture average back-propagation basin of attraction binary Boltzmann machine Boolean function brain Chapt coefficients configuration convergence corresponds denoted determined deviation distribution dynamics energy function equation error back-propagation error function evolution feed-forward feed-forward network finite Gaussian integrals genetic genetic algorithm given gradient Hebb's rule hidden layer hidden neurons hidden units Hopfield network implemented input neurons input patterns integration iteration large number learning process learning rule limit linear mapping matrix memory method neural net neural networks nonlinear operation optimal output layer output neurons parameter partition function pattern recognition perceptron performance phase Phys post-synaptic prediction probability problem random represent result S₁ saddle point signal simulated simulated annealing solution solve spin glasses stability statistical stochastic storage capacity stored patterns studied symmetric synaptic connections synaptic couplings synaptic strengths task temperature term threshold tion updating values variables weights
Popular passages
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