Neural Networks In Design And Manufacturing
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Neural Networks In Design And Manufacturing
Author: Yoshiyasu Takefuji
language: en
Publisher: World Scientific
Release Date: 1993-10-29
Over the past few years, there has been a surge of research activities on artificial neural networks. Although the thrust originally came from computer scientists and electrical engineers, neural network research has recently attracted researchers in the fields of operations research, operations management and industrial engineering.Despite the huge volume of recent publications devoted to neural network research, there is no single monograph addressing the potential roles of artificial neural networks for design and manufacturing.The focus of this book is on the applications of neural network concepts and techniques to design and manufacturing. This book reviews the state-of-the-art of the research activities, highlights the recent advances in research and development, and discusses the potential directions and future trends along this stream of research.The potential readers of this book will include, but are not limited to, beginners, professionals and practitioners in industries who are applying neural networks to design and manufacturing.The topics include conceptual design, group technology, process planning and scheduling, process monitoring and others.
Design and Implementation of Intelligent Manufacturing Systems
Author: Mohammed Jamshidi
language: en
Publisher: Pearson Education
Release Date: 1995-05-24
The introduction of artificial intelligence, neural networks, and fuzzy logic into industry has given a new perspective to manufacturing processes in the U.S. and abroad. To help readers keep pace, this book addresses topics of intelligent manufacturing from a variety of theoretical, empirical, design, and implementation perspectives.
Process Neural Networks
Author: Xingui He
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-07-05
"Process Neural Network: Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks and enhances the expression capability for practical problems, with broad applicability to solving problems relating to processes in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are closely examined. The application methods, network construction principles, and optimization algorithms of process neural networks in practical fields, such as nonlinear time-varying system modeling, process signal pattern recognition, dynamic system identification, and process forecast, are discussed in detail. The information processing flow and the mapping relationship between inputs and outputs of process neural networks are richly illustrated. Xingui He is a member of Chinese Academy of Engineering and also a professor at the School of Electronic Engineering and Computer Science, Peking University, China, where Shaohua Xu also serves as a professor.