A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
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A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
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Neural Networks for Optimization and Signal Processing A. Cichocki Warsaw University of Technology Poland R. Unbehauen Universit?t Erlangen-N?rnberg Germany Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the visualization and development of new techniques and architectures. Features include:
* A guide to the fundamental mathematics of neurocomputing.
* A review of neural network models and an analysis of their associated algorithms.
* State-of-the-art procedures to solve optimization problems.
* Computer simulation programs MATLAB, TUTSIM and SPICE illustrate the validity and performance of the algorithms and architectures described. The authors encourage the reader to be creative in visualizing new approaches and detail how other specialized computer programs can evaluate performance.
* Each chapter concludes with a short bibliography.
* Illustrative worked examples, questions and problems assist self study.
The authors' self-contained approach will appeal to a wide range of readers, including professional engineers working in computing, optimization, operational research, systems identification and control theory. Undergraduate and postgraduate students in computer science, electrical and electronic engineering will also find this text invaluable. In particular, the text will be ideal to supplement courses in circuit analysis and design, adaptive systems, control systems, signal processing and parallel computing. B.G. Teubner Stuttgart
* A guide to the fundamental mathematics of neurocomputing.
* A review of neural network models and an analysis of their associated algorithms.
* State-of-the-art procedures to solve optimization problems.
* Computer simulation programs MATLAB, TUTSIM and SPICE illustrate the validity and performance of the algorithms and architectures described. The authors encourage the reader to be creative in visualizing new approaches and detail how other specialized computer programs can evaluate performance.
* Each chapter concludes with a short bibliography.
* Illustrative worked examples, questions and problems assist self study.
The authors' self-contained approach will appeal to a wide range of readers, including professional engineers working in computing, optimization, operational research, systems identification and control theory. Undergraduate and postgraduate students in computer science, electrical and electronic engineering will also find this text invaluable. In particular, the text will be ideal to supplement courses in circuit analysis and design, adaptive systems, control systems, signal processing and parallel computing. B.G. Teubner Stuttgart
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First Sentence:
Studies of systems of nonlinear ordinary differential equations, difference equations, linear algebra and nonlinear programming are fundamental to the analysis and synthesis of artificial neural networks (ANNs). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
penalty function terms, exemplary computer simulation results, generalized learning algorithm, lateral synaptic weights, computer simulated trajectories, multicriterion optimization problem, neural network approach solve, bipartition problem, computational energy function, graph partition problem, linear processing units, above learning algorithm, computational temperature, blocking constraints, following energy function, analog neural network, linear complementarity problem, exemplary realization, sigmoid activation function, analog neurons, minimax problem, perceptron learning rule, parasitic oscillations, feasibility region, learning signal Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Academic Press, Electronics Letters, Journal of Neural Systems, Complex Systems, Technical Report, New Orleans, San Diego, American Institute of Physics, Neural Information Processing Systems, The Least Absolute Deviations, John Wiley, Optimization Networks Using, Van Loan, Artificial Neural Network Models, Biological Cybernetics, Computers Math, Matrix Anal New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Studies of systems of nonlinear ordinary differential equations, difference equations, linear algebra and nonlinear programming are fundamental to the analysis and synthesis of artificial neural networks (ANNs). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
penalty function terms, exemplary computer simulation results, generalized learning algorithm, lateral synaptic weights, computer simulated trajectories, multicriterion optimization problem, neural network approach solve, bipartition problem, computational energy function, graph partition problem, linear processing units, above learning algorithm, computational temperature, blocking constraints, following energy function, analog neural network, linear complementarity problem, exemplary realization, sigmoid activation function, analog neurons, minimax problem, perceptron learning rule, parasitic oscillations, feasibility region, learning signal Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Academic Press, Electronics Letters, Journal of Neural Systems, Complex Systems, Technical Report, New Orleans, San Diego, American Institute of Physics, Neural Information Processing Systems, The Least Absolute Deviations, John Wiley, Optimization Networks Using, Van Loan, Artificial Neural Network Models, Biological Cybernetics, Computers Math, Matrix Anal New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 22
books:
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53
books
cite this book:
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- Numerical Optimization (Springer Series in Operations Research and Financial Engineering) by Jorge Nocedal on page 37, page 229, and page 421
- Neural Information Processing Systems by Dana Z. Anderson on page 305, and page 514
- Practical Methods of Optimization by R. Fletcher on page 37, and page 229
- System Identification: Theory for the User (2nd Edition) by Lennart Ljung on page 476
- Numerical Linear Algebra, Digital Signal Processing and Parallel Algorithms (Nato ASI Series Series F, Computer and Systems Sciences Vol. 70) by Gene H. Golub on page 356
See all 22 books this book cites
- Applied Neural Networks for Signal Processing by Fa-Long Luo on page 72, and page 334
- Hardware Implementation of Intelligent Systems by Horia-Nicolai Teodorescu on page 192, and page 216
- Recurrent Neural Networks: Design and Applications (International Series on Computational Intelligence) by Larry Medsker on page 42, and page 127
- Pattern Recognition by Sergios Theodoridis on page 133
- Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Signals and Communication Technology) by Peter Stavroulakis in Front Matter
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