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by Richard K. Miller,J. Reinhardt
Download Neural Networks: An Introduction/With Diskette (Physics of Neural Networks) fb2
Computer Science
  • Author:
    Richard K. Miller,J. Reinhardt
  • ISBN:
    0387523804
  • ISBN13:
    978-0387523804
  • Genre:
  • Publisher:
    Springer Verlag; Pap/Dskt edition (May 1, 1991)
  • Pages:
    266 pages
  • Subcategory:
    Computer Science
  • Language:
  • FB2 format
    1634 kb
  • ePUB format
    1143 kb
  • DJVU format
    1285 kb
  • Rating:
    4.7
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    123
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Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are . The software is included on a 3 1/2-inch MS-DOS diskette

Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. The software is included on a 3 1/2-inch MS-DOS diskette. The source code can be modified using Borland's TURBO-C . compiler, the Microsoft C compiler (. ), or compatible compilers.

Neural Networks presents concepts of neural-network models and . Neural Networks: An Introduction Physics of Neural Networks.

Neural 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 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.

Success was sometimes measured on the ability of the network to replicate the required mapping  .

Neural Networks: An Introduction/With Diskette (Physics of Neural Networks)

Neural Networks: An Introduction/With Diskette (Physics of Neural Networks). by Berndt Müller and Joachim Reinhardt. Neural 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.

Neural Networks presents concepts of neural-network models and techniques of. .

Neural 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.

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Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques are also introduced. Thinking About the Brain by William Bialek - arXiv We all are fascinated by the phenomena of intelligent behavior, as generated by our own brains

They modeled a simple neural network with electrical circuits. In 1949, Donald Hebb reinforced the concept of neurons in his book, The Organization of Behavior. It pointed out that neural pathways are strengthened each time they are used

They modeled a simple neural network with electrical circuits. It pointed out that neural pathways are strengthened each time they are used.

applications of adaptive, artificial neural networks. The excitement, and the frustration, of these topics is that they span such a broad range of disciplines including mathematics, statistical physics and chemistry, neurology and neurobiology, and computer science and electrical engineering as well as cognitive psychology, artificial intelligence, and philosophy.

Analysis of synfire chains. Network: Computation in Neural Systems, Vol. 6, Issue. Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding. Nonequilibrium neural network with competing dynamics. Gerstner, Wulfram 1995. Time structure of the activity in neural network models. Bicout, Dominique J. and Field, Martin J. 1995. 8, Issue. Physica A: Statistical Mechanics and its Applications, Vol. 253, Issue.

The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to "neural" information processing, i.e. associative memory, perceptrons, feature-sensitive networks, learning strategies, and practical applications. - Part 2 covers more advanced subjects such as spin glasses, the mean-field theory of the Hopfield model, and the space of interactions in neural networks. - The self-contained final part discusses seven programs that provide practical demonstrations of neural-network models and their learning strategies. Ample opportunity is given to improve and modify the source codes. The software is included on a 5 1/4 inch MS DOS diskette and can be run using Borland's TURBO C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.