Neural Network Design, 2/e (Paperback)

Martin T Hagan, Howard B Demuth, Mark H Beale, Orlando De Jesús

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商品描述

This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.

商品描述(中文翻譯)

這本書是由 MATLAB 的神經網絡工具箱的作者撰寫,提供了對基本神經網絡結構和學習規則的清晰和詳細的介紹。在這本書中,作者強調了對主要神經網絡、訓練方法以及它們在實際問題中的應用的一致性介紹。特點包括對前饋網絡(包括多層和基於半徑的網絡)和循環網絡的訓練方法的廣泛涵蓋。除了共軛梯度和Levenberg-Marquardt變體的反向傳播算法外,本書還介紹了貝葉斯正則化和提前停止等方法,以確保訓練網絡的泛化能力。還介紹了關聯和競爭網絡,包括特徵映射和學習向量量化,並使用簡單的構建塊進行解釋。還包括一章實用的訓練技巧,用於函數逼近、模式識別、聚類和預測,以及五章詳細的實際案例研究。書中還包含詳細的示例和大量解決的問題。可以從 hagan.okstate.edu/nnd.html 下載幻燈片和全面的演示軟件。