Principles of Neurocomputing for Science and Engineering
Seiten
2000
McGraw-Hill Professional (Verlag)
978-0-07-025966-9 (ISBN)
McGraw-Hill Professional (Verlag)
978-0-07-025966-9 (ISBN)
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Neurocomputing plays an important role in solving certain problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis. This book covers artificial neural networks with a focus on neurocomputing. It includes many examples and end-of-chapter problems.
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.
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.
Fred Ham is a professor at the Florida Institute of Technology. Ivica Kostanic works at SAFCO Technologies Inc.
1 Introduction to Neurocomputing2 Fundamental Neurocomputing Concepts3 Mapping Networks4 Self-Organizing Networks5 Recurrent Networks and Temporal Feedforward Networks6 Neural Networks for Optimization Problems7 Solving Matrix Algebra Problems with Neural Networks8 Solution of Linear Algebraic Equations Using Neural Networks9 Statistical Methods Using Neural Networks10 Identification, Control, and Estimation Using Neural NetworksAppendix Mathematical Foundation for Neurocomputing
| Erscheint lt. Verlag | 16.10.2000 |
|---|---|
| Sprache | englisch |
| Maße | 188 x 234 mm |
| Gewicht | 1134 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Informatik ► Weitere Themen ► CAD-Programme | |
| Naturwissenschaften ► Biologie ► Humanbiologie | |
| Naturwissenschaften ► Biologie ► Zoologie | |
| ISBN-10 | 0-07-025966-6 / 0070259666 |
| ISBN-13 | 978-0-07-025966-9 / 9780070259669 |
| Zustand | Neuware |
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