《Neural and Adaptive Systems: Fundamentals through Simulations（英语）》

José C. Principe (作者), Neil R. Euliano (作者), W. Curt Lefebvre (作者)

基本信息

• 出版社: John Wiley & Sons; 1 (1999年12月21日)
• 平装: 676页
• 语种： 英语
• ISBN: 0471351679
• 条形码: 9780070487604
• 商品尺寸: 19.7 x 3 x 24.7 cm
• 商品重量: 1.1 Kg
• ASIN: 0471351679

概况

Develop New Insight into the Behavior of Adaptive Systems
This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions.
This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text.
发展适应系统的行为的新的洞察
这一类的互动图书和光盘将帮助你更好地了解自适应系统的行为。作为一个项目的一部分，旨在创新的科学和工程中的自适应系统的教学，它结合了神经网络和自适应滤波器的概念到一个共同的框架。它开始解释自适应线性回归的基本原理，并建立在这些概念上探索模式分类，函数逼近，特征提取，和时间序列建模/预测。文本与业界标准的神经网络自适应系统模拟器类/集成。
这允许作者展示和加强关键概念，使用超过200个互动的例子。这些例子中的每一个都是“活的”，允许用户改变参数和实验的第一手与现实世界的自适应系统。这创造了一个强大的学习环境，通过可视化和实验。文本的主要特征文本和光盘结合成为一个互动的学习工具。

目录

Data Fitting with Linear Models.
Pattern Recognition.
Multilayer Perceptrons.
Designing and Training MLPs.
Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines.
Hebbian Learning and Principal Component Analysis.
Competitive and Kohonen Networks.
Principles of Digital Signal Processing.
Adaptive Filters.
Temporal Processing with Neural Networks.
Training and Using Recurrent Networks.
Appendices.
Glossary.
Index.