PDF Ebook Neural Networks for Intelligent Signal P (Series on Innovative Intelligence), by Anthony Zaknich
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Neural Networks for Intelligent Signal P (Series on Innovative Intelligence), by Anthony Zaknich
PDF Ebook Neural Networks for Intelligent Signal P (Series on Innovative Intelligence), by Anthony Zaknich
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This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.
- Sales Rank: #5203408 in Books
- Published on: 2003-01-24
- Original language: English
- Number of items: 1
- Dimensions: 1.19" h x 9.50" w x 6.00" l, .0 pounds
- Binding: Hardcover
- 508 pages
Most helpful customer reviews
7 of 7 people found the following review helpful.
Neural Networks for Intelligent Signal Processing
By Stanley McGibney
***** Stanley McGibney, Consultant Mechanical Engineer
I found this book to be significantly different in its treatment of neural networks for signal processing and pattern recognition. It deals very ably not only with essential theory but also with basic practical issues, often missing from other books on the subject, that significantly enhance understanding and application. Zaknich has included a nice guide and design approach to successful application of neural networks, which is supplemented by frequent tips and a variety of worked application examples.
The book is much more than a good introduction to neural networks. It also includes a class of neural networks that Zaknich has developed and worked on over a decade that he refers to as common bandwidth spherical basis function neural networks. This is based on a generalization called the Modified Probabilistic Neural Network (MPNN) that encompasses Donald Specht's Probabilistic and General Regression Neural Networks. He has continued to develop the MPNN in a number of very practical directions that allows it to used for a wide range of engineering problems. He seems to favour applications related to underwater acoustic signal processing but the methods and approaches that he offers are suited to many other non-linear problems found in engineering and other disciplines.
The book includes a very interesting discussion on intelligent signal processing. Zaknich talks about what he calls hyperspace signal processing in the context of the MPNN and other classical filtering structures that gives an interesting view of some of the basic issues involved. He suggests at least one possible generic approach to non-linear signal processing based on Vapnik's Support Vector Machine that has a structural similarity to the MPNN.
This book is a gem that shines in its clarity beyond many other books on neural networks that I have struggled with in an attempt to understand the subject well enough to apply it.
6 of 6 people found the following review helpful.
A must for neural network engineers and students
By Claas Richter
Anthony Zaknich wrote a book that provides the reader with a very broad knowledge about neural networks especially for signal processing. Fundamental facts are extracted and presented in a form that is very easy to read, such as listings, keywords, main formulas, diagrams and results from experiments. Zaknich avoided going into too much theoretical details which might only confuse beginners. But also for advanced and professionals his book is a good source of well-structured basic knowledge. This is the difference compared to other books about neural networks since the reader finds the essence that is crucial to understanding the basics. A huge selection of literature references enables the reader to quickly continue deeper studies about interesting topics. Furthermore, Zacknich' provides examples and results from experiments and simulations which he has done by himself for the most parts. He compares different neural network types and configurations for various real-world examples. These comparisons and results were a very valuable help for me in order to understand the differences and characteristics among the vast number of various network types.
Finally, Zaknich's book helps me very much in my job for that it is very important to have application results and "benchmark-like" comparisons. His explanations are often with reference to his own developed Modified Probabilistic Neural Network (MPNN) and Advanced MPNN that animated me to carry out some tests based on his MPNN. Another idea in his book is the Integrated Sensory Intelligent System (ISIS) that he introduces. I would highly appreciate if Zaknich releases new books especially on the field of signal processing and his idea of the ISIS.
Zaknich knows how to guide the reader through the different topics and not to bore him by long theoretical sequences and formulas. There is already a wide range of available literature that explains topics, such as classifications, control systems, robots etc. in more detail. Interesting quotations from different sources of literature attached to each chapter makes the book even more than merely a scientific book.
Claas Richter
Silicann Technologies (Japan/Germany)
5 of 5 people found the following review helpful.
Neural Networks for Engineers
By Yvonne Power (phd student)
Zaknich's book provided me with the necessary theory and detail to enable me to develop a neural network application as part of my phd. I thoroughly recommend this text for anyone wishing to develop and use neural networks, particularly for engineering applications. I found that while most books focussed on either the theory of neural networks or selected applications, Zaknich's text provided a comprehensive coverage of both theory and application providing a sound basis for understanding and applying neural networks. Topics ranged from the applicability of neural networks through to data collection, data conditioning and the final implementation.
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