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

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)
? 平裝: 676頁
? 語種： 英語
? ISBN: 0471351679
? 條形碼: 9780070487604
? 商品尺寸: 19.7 x 3 x 24.7 cm
? 商品重量: 1.1 Kg
? ASIN: 0471351679

概況

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.
發(fā)展適應(yīng)系統(tǒng)的行為的新的洞察
這一類的互動圖書和光盤將幫助你更好地了解自適應(yīng)系統(tǒng)的行為。作為一個項目的一部分，旨在創(chuàng)新的科學(xué)和工程中的自適應(yīng)系統(tǒng)的教學(xué)，它結(jié)合了神經(jīng)網(wǎng)絡(luò)和自適應(yīng)濾波器的概念到一個共同的框架。它開始解釋自適應(yīng)線性回歸的基本原理，并建立在這些概念上探索模式分類，函數(shù)逼近，特征提取，和時間序列建模/預(yù)測。文本與業(yè)界標(biāo)準(zhǔn)的神經(jīng)網(wǎng)絡(luò)自適應(yīng)系統(tǒng)模擬器類/集成。
這允許作者展示和加強關(guān)鍵概念，使用超過200個互動的例子。這些例子中的每一個都是“活的”，允許用戶改變參數(shù)和實驗的第一手與現(xiàn)實世界的自適應(yīng)系統(tǒng)。這創(chuàng)造了一個強大的學(xué)習(xí)環(huán)境，通過可視化和實驗。文本的主要特征文本和光盤結(jié)合成為一個互動的學(xué)習(xí)工具。

目錄

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.