Principles of Neurocomputing for Science & Engineering

This exciting new text covers artificial neural networks, but more specifically, neurocomputing. Neurocomputing is concerned with processing information, which involves a learning process within an artificial neural network architecture. This neural architecture responds to inputs according to a defined learning rule and then the trained network can be used to perform certain tasks depending on the application. Neurocomputing can play an important role in solving certain problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis. "Principles of Neurocomputing for Science and Engineering," unlike other neural networks texts, is written specifically for scientists and engineers who want to apply neural networks to solve complex problems. For each neurocomputing concept, a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm. The book is primarily intended for graduate-level neural networks courses, but in some instances may be used at the undergraduate level. The book includes many detailed examples and an extensive set of end-of-chapter problems.

這本令人興奮的新書涵蓋了人工神經網絡，更具體地說是神經計算。神經計算關注的是處理信息，其中包括在人工神經網絡結構內的學習過程。這種神經結構根據一個定義好的學習規則對輸入作出反應，然後訓練好的網絡可以根據應用執行特定任務。神經計算在解決模式識別、優化、事件分類、非線性系統的控制和識別以及統計分析等特定問題上扮演著重要角色。《科學與工程的神經計算原理》不同於其他神經網絡教材，專門為希望應用神經網絡解決複雜問題的科學家和工程師撰寫。對於每個神經計算概念，書中都提供了堅實的數學基礎，並附有相應架構和相關訓練算法的示例。該書主要面向研究生級別的神經網絡課程，但在某些情況下也可用於本科水平。該書包含許多詳細的示例和大量章末問題。