Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
Springer Science & Business Media, 31 дек. 1992 г. - Всего страниц: 440
1.1 Overview We are living in a decade recently declared as the "Decade of the Brain". Neuroscientists may soon manage to work out a functional map of the brain, thanks to technologies that open windows on the mind. With the average human brain consisting of 15 billion neurons, roughly equal to the number of stars in our milky way, each receiving signals through as many as 10,000 synapses, it is quite a view. "The brain is the last and greatest biological frontier", says James Weston codiscoverer of DNA, considered to be the most complex piece of biological machinery on earth. After many years of research by neuroanatomists and neurophys iologists, the overall organization of the brain is well understood, but many of its detailed neural mechanisms remain to be decoded. In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psy chologists have taken a top-down approach of studying brain func tions from the cognitive and behavioral level. While these two ap proaches are gradually converging, it is generally accepted that it may take another fifty years before we achieve a solid microscopic, intermediate, and macroscopic understanding of brain.
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Neural Network Architectures and Learning Schemes
ELEANNE Efficient LEarning Algorithms for Neural NEtworks
Fast Learning Algorithms for Neural Networks
ALADIN Algorithms for Learning and Architecture DetermINation
Performance Evaluation of Singlelayered Neural Networks
Highorder Neural Networks and Networks with Composite Key Patterns
Applications of Neural Networks A Case Study
Applications of Neural Networks A Review
Future Trends and Directions
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adaptation cycle ALADIN analog output approximation Back Propagation algorithm binary output Boltzmann machine capacity ratio chapter composite key patterns Consider convergence corresponding defined Delta Rule efficient market hypothesis eigenvalue elements Error Back Propagation evaluation Fast Back Propagation Fast ELEANNE feed-forward neural networks first-order neural network GNNP gradient descent Grossberg Hamming distance Hessian matrix hidden layer hidden units IEEE initial adaptation cycles input patterns Kohonen Kronecker product layer of hidden learning algorithms learning rate learning scheme linear lower network Lyapunov function matrix of synaptic minimizing multi-layered neural network networks of order neural net neural networks trained neuron nonlinear number of adaptation number of association number of hidden objective function optimal matrix outer-product rule output units parameter performance pollution sources presented problem recursive resulting Sejnowski single-layered neural networks stored patterns synaptic weights vpq tion Touretzky trained neural network trained with respect update equation Vi,k yi,k yp,k
Стр. 402 - L. Personnaz, I. Guyon and G. Dreyfus, "Information Storage and Retrieval in SpinGlass like Neural Networks", J.
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Rough Sets in Knowledge Discovery: Applications, case studies, and software ...
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Недоступно для просмотра - 1998