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Fundamentals of neural networks : architectures, algorithms, and applications /

Guardado en:
Autor Principal: Fausett, Laurene.
Formato: Libro
Idioma:English
Publicado: Englewood Cliffs, NJ : Prentice-Hall, c1994.
Materias:
Redes neuronales (Computación)
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082 0 4 |a 006.3 
100 1 |a Fausett, Laurene.  |9 127981 
245 1 0 |a Fundamentals of neural networks :  |b architectures, algorithms, and applications /  |c Laurene Fausett. 
260 |a Englewood Cliffs, NJ :  |b Prentice-Hall,  |c c1994. 
300 |a xvi, 461 p. :  |b il. ;  |c 23 cm. 
504 |a Incluye referencias bibliográficas (p. 437-447) e índice. 
505 0 |a Ch. 1. Introduction -- 1.1. Why Neural Networks and Why Now? -- 1.2. What Is a Neural Net? -- 1.3. Where Are Neural Nets Being Used? -- 1.4. How Are Neural Networks Used? -- 1.5. Who Is Developing Neural Networks? -- 1.6. When Neural Nets Began: the McCulloch-Pitts Neuron -- Ch. 2. Simple Neural Nets for Pattern Classification -- 2.1. General Discussion -- 2.2. Hebb Net -- 2.3. Perceptron -- 2.4. Adaline -- Ch. 3. Pattern Association -- 3.1. Training Algorithms for Pattern Association -- 3.2. Heteroassociative Memory Neural Network -- 3.3. Autoassociative Net -- 3.4. Iterative Autoassociative Net -- 3.5. Bidirectional Associative Memory (BAM) -- Ch. 4. Neural Networks Based on Competition -- 4.1. Fixed-Weight Competitive Nets -- 4.2. Kohonen Self-Organizing Maps -- 4.3. Learning Vector Quantization -- 4.4. Counterpropagation -- Ch. 5. Adaptive Resonance Theory -- 5.1. Introduction -- 5.2. Art1 -- 5.3. Art2 -- Ch. 6. Backpropagation Neural Net -- 6.1. Standard Backpropagation -- 6.2. Variations -- 6.3. Theoretical Results -- Ch. 7. A Sampler of Other Neural Nets -- 7.1. Fixed Weight Nets for Constrained Optimization -- 7.2. A Few More Nets that Learn -- 7.3. Adaptive Architectures -- 7.4. Neocognitron. 
653 |a Redes neuronales (Computación)  |9 127987 
859 |a AR-BbBCA  |h 006.3  |i F267  |p 2011/5907  |b BIB. AGRONOMIA 

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