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Principles of Neurocomputing for Science and Engineering, by Fredric M. Ham, Ivica Kostanic
Free PDF Principles of Neurocomputing for Science and Engineering, by Fredric M. Ham, Ivica Kostanic
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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.
- Sales Rank: #13114156 in Books
- Published on: 2001-03-01
- Original language: English
- Dimensions: 10.28" h x .79" w x 7.48" l,
- Binding: Paperback
- 642 pages
About the Author
Fred Ham is a professor at the Florida Institute of Technology. Ivica Kostanic works at SAFCO Technologies Inc.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Best neural network textbook for beginners
By C. Chen
This is definitely a must read for all NN beginners. In my opinion, this book is very well organized. First, it puts theoretical stuff and applicaiton examples into separate parts (Part I&II). Second, in Part I, it starts with basic constituents of NNs: one neuron NN, activation fucntion, learning rule,...and then covers NNs of more large-scale types. Within each type, it describes the most rudementary or prototype NN first, supplements info regarding other variants next, and mentions the issues to be encountered for actual implemention. This all makes this book so easy to follow. I can quickily decide whether I can skip the coming section/subsection and no worry of discontinuity as I read along.
10 of 11 people found the following review helpful.
For beginners and graduate students
By Sungjin Park
I strongly recommend this text for beginners and graduate students who want to understand Neural Networks. First, the text book explains the why, where, and how to apply Neural Networks, which is the most important point for understanding Neural Networks. Second, mathematical proofs, clear description of algorithms and MATLAB codes encourage me to do it myself. And a lot of mathematical foundation in the Appendix allow me to understand quite easily complex mathematical concepts.
1 of 2 people found the following review helpful.
For beginners and graduate students
By Sungjin Park
I strongly recommend this text for beginners and graduate students who want to understand Neural Networks. First, the text book explains the why, where, and how to apply Neural Networks, which is the most important point for understanding Neural Networks. Second, mathematical proofs, clear description of algorithms and MATLAB codes encourage me to do it myself. And a lot of mathematical foundation in the Appendix allow me to understand quite easily complex mathematical concepts.
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