Biblioteca de Agronomía "Dr. Ovidio Núñez" Departamento de Agronomía - UNS · Catálogo |
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LDR | ·····nam##22·····5a#4500 |
001 | 008494 |
005 | 20170928113026.0 |
008 | 130809s1994####xxua#####b####000#0#eng#d |
020 | ## | $a 0133341860 |
082 | 04 | $a 006.3 |
100 | 1# | $a Fausett, Laurene. |
245 | 10 | $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) |