This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples.
This volume includes Radial Basis Function networks, the Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variants of Hamming and Hopfield neural networks, discrete time synchronous multilevel neural systems with reduced VLSI demands, probabilistic design techniques, time-based techniques, techniques for reducing physical realization requirements, and applications to finite constraint problems.
A unique and comprehensive reference for a broad array of algorithms and architectures, this book will be of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering.
- Radial Basis Function networks
- The Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks
- Weight initialization
- Fast and efficient variants of Hamming and Hopfield neural networks
- Discrete time synchronous multilevel neural systems with reduced VLSI demands
- Probabilistic design techniques
- Time-based techniques
- Techniques for reducing physical realization requirements
- Applications to finite constraint problems
- Practical realization methods for Hebbian type associative memory systems
- Parallel self-organizing hierarchical neural network systems
- Dynamics of networks of biological neurons for utilization in computational neuroscience
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Kim, Park, Oh, and Han, The Synthesis of Three-Layer Threshold Networks
Lehtokangas, Salmela, Saarinen, and Kaski, Weight Initialization Techniques
Meilijson, Ruppin, and Sipper, Fast Computation in Hamming and Hopfield Networks
Si and Michel, Multilevel Neurons
Watanabe and Fukumizu, Probabilistic Design
Tom and Tenorio, Short Time Memory Problems
Chung and Tsai, Reliability Issue and Quantization Effects in Optical and Electronic Network Implementations of Hebbian-Type Associative Memories
Monfroglio, Finite Constraint Satisfaction
Chu, Estimating the Dimensions of Manifolds Using Delaunay Diagrams
Ersoy, Parallel, Self-Organizing, Hierarchical Neural Network Systems
Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.