Neural Networks Books

This exceptional book provides a broad overview of the methods of extracting the knowledge (or in other words building model/system/theory) from the data in various areas: from information theory and artificial intelligence to genetics. It can be very useful for biologists, who wish to use modern computational methods for analysis of microarray data, regulatory networks, cancer, analysis of clinical trials, etc.

The first part (first seven chapters) of the book is devoted to the methods used in connectionists systems and here readers can find detailed description of the algorithms. In the second part (six chapters), which represents application of these methods, the book has a chapter devoted to the data analysis, modeling, and knowledge discovery in bioinformatics so it can be interesting for the biologists. This chapter describes how the neural network paradigm can be used in molecular biology and, in particular, for analysis in relatively new area -- microarray technology. The huge amount of data that were obtained in this area is still waiting for the efficient methods of knowledge extracting. In this chapter readers can also find the examples of using evolving connectionists learning systems for solving the problems of finding the patterns from DNA/RNA sequences, identification of intron/exon binding sites, gene profiling, protein structure prediction and dynamic cell modeling.

This excellent book is full of interesting examples, classification schemes, and figures.
Although this book will be more interesting for readers, which have been working in networking, it can be useful also for all researchers and students and any type of readers interesting in data analysis. This book is outstanding introduction for readers unfamiliar with the learning systems. The extended glossary and full-length reference list will help a lot for readers inexperienced in this area.

I found this book to be a landmark contribution to the state-of-the-art in neural networks pardigm. It offers some exciting neural network topologies and a distinctly new kind of thinking -'local learning' in neural networks. The author Prof. Nik Kasabov deserves to be congratulated for writing this excellent book. His explanation throughout the book is very lucid and to the point. He introduces the concept of "evolving connectionism" in a succinct way. He included a rich assortment of connectionist methods, right from the scratch, with a clear exposition of the underlying training algorithms. The applications presented in the latter part of the book are as diverse as bioinformatics, financial engineering, speech recognition, brain study and image & video data processing. The authority with which these topics are presented speaks volumes of the enormous research work undertaken by Prof. Kasabov and his students. The references and extended glosary provided at the end are extremely useful to the reader. Another important aspect of this book is that it is suitable for all levels of readers such as student, researcher and practitioner. I started teaching some aspects of this book from this semester onwards. It is well received by the students. It must be in the shelves of those who look for the latest research in the area of neural networks. I enjoyed reading this book. Finally, if the phrase "real-time neural networks" is also added in the tag line (sub title) of the book, it could attract more users.

This book deepens concepts about fuzzy theory and fuzzy control. It is a useful tool to develop applications in fuzzy control. Otherwise this one presents a basic introduction to neural network, furthermore the theory about neural-fuzzy and fuzzy-neural is presented in basic form. The book contains a large number of examples about all items and Matlab software is used in many of they. I would catalog this book in medium level of deepening. Topics about fuzzy control are tried deeper than neuro-fuzzy, fuzzy-neural topics.

Fascinating reading. I had trouble putting it down at times, which is a lot to be said for a text book! Although I had lots of experience with classical control methods I hadn't studied neural or fuzzy systems before. I found the book gave me an excellent introduction to the topics in a highly readable format and yet also gave me the details mathematics required to be able to be able to program fuzzy and neural systems myself. It also had examples using the Matlab toolboxes which was useful.

Gain access to fuzzy logic algorithms, design guidelines, and current applications examples from signal processing and power systems to home appliance design and manufacturing with this key handbook! Featuring contributions from top names in the field, this handbook offers an in-depth understanding of the major research and activities in fuzzy logic and neural networks.

An excellent reference on Fuzzy Logic and Neural Networks.

This books covers fundamentals topics like:
- Principles and Algorithms.
- Applications.
- Architectures and Systems.

An excellent textbook edited by IEEE and with contributions from many experts on these fields.

I have read about half the book so far, and like it more or less. It takes a *long* time to get a good definition of Fuzzy Logic, its components, and its process. It lacks a good section on just diagramming the process flow, however the components have a decent amount of detail dedicated to them. Some of the diagrams are self explanatory, but too much detail in text for them.

This is one of my Fuzzy Logic book's. The C++ OOP code inside will make your life simple. Easy deign can be done by simply adapt the C++ code. Usefull information and review was made by the author on the topic of Fuzzy Logic with microcontroller system !! Well if you need a practical book on Fuzzy Logic .... this is the one !!

This is a good book, but potential buyers should realize that it is aimed at researchers, not traders. It would take considerable work to rediscover the successful neural net discussed in the book, and there is no guarantee that the net would do well in real trading because the author does not seem to have taken slippage and commissions into account.

This book contains a number of new and interesting insights into the application of neural networks and genetic algorithms for finance. The idea of using "network regression pruning" looks extremely powerful and it would be very interesting to see this compared with other network pruning techniques. An aspect also worth highlighting is the detailed treatment of the financial experiments which appear to have been conducted in a rigorous and careful manner. I have attended many seminars and talks in which the experimentation for financial applications has been extremely poor. In many instances there is a failure to establish objective test criteria. In this work a single test run on ten years' worth of data is conducted and the algorithm performs well, contradicting all forms of the efficient market hypothesis. The level of detail also allows the reader to reconstruct the algorithm and methods for their own use. Other books on this subject should be as clear and as open.

It have a whole section about graphs, what's not normal. It have an excelent and didatic way of explaining things, with lots of examples, exercises and graphics.

It's undoubtely the best book on ANN I've ever seen.

this book is for the advanced user. It covers in-depth the concepts of neural networks, Artificial intelligence, and intelligent systems. A must have for all those artificial intelligent buffs...

Extremely expensive, but required by my teacher in grad school in class "Neural Networks". Good, comprehensive source of knowledge to understand NN. However, If you want to use NN to solve just practical problems, read Help in Matlab nntool (2007a), which is decently explained, and follow examples.

I found Dr. Samarasinghe's very easy to understand yet very comprehensive in its coverage of neural networks. The hand calculations really helped me see how the algorithms are applied to real-world problems. This is one of the best books on the subject that I own, and I own a bunch of them. I highly recommend it!

This is a unique if somewhat older book on the subject of applying neural networks to applications in the fields of image processing and computer vision to solve very specific problems. It is just a collection of papers, so there is no tutorial included on the basics of neural networks themselves. Thus you should already be familiar with the topic including the construction of neural networks via Matlab or whatever toolkit with which you feel comfortable, and you should also already be aware of the limitations and capabilities of neural networks.

Since this is a compiled work, the various papers vary tremendously in quality. Personally, I thought "Perception: A Biological Perspective", the paper on "Figure-Ground Separation of Connected Scenic Figures", and "A New Approach to Shape From Shading" were the best papers with the clearest explanations and best figures. However, your mileage may vary depending on your particular interests. Some papers go as far as giving algorithmic steps, and others are little more than a pure text explanation. Quite a few papers might be interesting to the student of neuroscience more than the computer scientist as theories are presented more as models of biological vision than algorithms that can be coded. An example of this would be "Neural Circuits for Visual Attention in the Primate Brain".

Parts of this book are online for your examination, so since most of the used copies are going for such high prices, you might want to look at the several online chapters before purchasing. Just put the book's title in quotations and do a Google search. It should be the third link on the returned list of addresses.

I'm gonna be short, if you're looking for some very complete literature in the field, you just found it. Although it's not very recent, it covers all the topics you may need for research. The same ideas that cotinue evolving.

The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion of exhaustive references is particularly useful for the prospective researcher, but, at the risk of sounding ungrateful, I'm curious as to why paper titles were not included in the citations? Overall though, a very good reference.

This is a wonderfully written, and excellent book. It assumes only minimal background knowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautiful mathematics.

This is another book where the capabilities of Mathematica are put to good use. Clear explanations and code make it a joy to go through and do all the calculational stuff. Helps even quite experienced people to visualise some of the concepts they may not be experienced with. All the basic models are dealt with. The last chapter on genetic algorithms is a bonus.

Overall, I am quite happy with the book. It does exactly as it describes...shows the reader how to use mathematica to simulate several types of Neural Networks. The code is clear, fairly short and the example networks fun to work though. The flexibilty of Mathematica made it a simple task to view what the networks were doing and thus made the networks easier to understand.

My only complaint is that the book is too short. Part of this complaint is that I really enjoyed playing with the example nets and hated to see it end. However, only about 8 networks are mentioned and each is covered in 20-30 pages...program code included. I wish the author had time to double or triple the size of the book to cover the nets more thoroughly and to cover others.

The book is also a bit shy on the mathematical treatment of the networks. It does have some math, but the derivations included are not that rigorous. I have supplemented this book with others to cover the theory.

Nevertheless, If you're wanting to use Mathematica for Neural Networking and you're having difficulty getting started, this book is worth the price.