This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.
@introbul:Key Features @bul:* Recurrent methods
- Boltzmann machines
- Constructive learning with methods for the reduction of complexity in neural network systems
- Modular systems
- Associative memory
- Neural network design based on the concept of the Inductive Logic Unit
- Data classification
- Integrated neuron model systems that function as programmable rational approximators @text:With numerous examples to enhance the text, practitioners, researchers, and students in engineering and computer science will find Implementation Techniques a uniquely comprehensive and powerful reference source
Practitioners, research workers, academicians, and students in mechanical, electrical, industrial, manufacturing, and production engineering, as well as computer science and engineering.
Bianchini, Frasconi, Gori, and Maggini, Optimal Learning in Artificial Neural Networks: A Theoretical View. Kanjilal, Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems. Museli, Sequential Constructive Techniques. Yu, Xu, and Wang, Fast Backpropagation Training Using Optimal Learning Rate and Momentum. Angulo and Torras, Learning of Nonstationary Processes. Schaller, Constraint Satisfaction Problems. Yang and Chen, Dominant Neuron Techniques. Lin, Chiang, and Kim, CMAC-based Techniques for Adaptive Learning Control. Deco, Information Dynamics and Neural Techniques for Data Analysis. Gorinevsky, Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems.
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- © Academic Press 1998
- 13th November 1997
- Academic Press
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- Hardcover ISBN:
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@qu:"....this book would make a valuable addition to most libraries(personal or institutional)...." "...its depth and breadth and leading edge flavor will of of interest to many neural network engineers." @source:--Dan Simon, Innovatia Software, CONTROL ENGINEERING PRACTICE, Issue 7, 1999.