Principles of Artificial Neural Networks (3rd Edition) (Advanced Series in Circuits and Systems)

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It is hoped that these text and this enhanced Edition can serve to show and to persuade scientists, engineers and program developers in areas ranging from medicine to nance and beyond, of the value and the power of ANN in problems that are ill-dened, highly non-linear, stochastic and of time-varying dynamics and which often appear to be beyond solution.

Additional corrections and minor modications are also included, as are other updates based on recent developments including those relating to the authors research. The Second Edition contains certain changes and additions to the First Edition. Apart from corrections of typos and insertion of minor additional details that I considered to be helpful to the reader, I decided to interchange the order of Chapters 4 and 5 and to rewrite Chapter 13 so as to make it easier to apply the LAMSTAR neural network to practical applications. I also moved the Case Study 6.

D to become Case Study 4. A, since it is essentially a Perceptron solution. I consider the Case Studies important to a reader who wishes to see a concrete application of the neural networks considered in the text, including a complete source code for that particular application with explanations on organizing that application.

To allow better comparison between the various neural network architectures regarding performance, robustness and programming eort, all Chapters dealing with major networks have a Case Study to solve the same problem, namely, character recognition.

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Consequently, the Case studies 5. A previously, 4. A, since the order of these chapters is interchanged , 6. A previously, 6. A, have all been replaced with new and more detailed Case Studies, all on character recognition in a 6 6 grid. Case Studies on the same problem have been added to Chapter 9, 12 and 13 as Case Studies 9.

A, A and A the old Case Studies 9. A now became 9. B and Also, a Case Study 7. Other Case Studies remained as in the First Edition. I hope that these updates will add to the readers ability to better understand what Neural Networks can do, how they are applied and what the dierences are between the dierent major architectures. I feel that this and the case studies with their source codes and the respective code-design details will help to ll a gap in the literature available to a graduate student or to an advanced undergraduate Senior who is interested to study articial neural networks or to apply them.

It and its Case Studies xi.

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This book evolved from the lecture notes of a rst-year graduate course entitled Neural Networks which I taught at the Department of Electrical Engineering and Computer Science of the University of Illinois at Chicago over the years Whereas that course was a rst-year graduate course, several Senior-Year undergraduate students from dierent engineering departments, attended it with little diculty. It was mainly for historical and scheduling reasons that the course was a graduate course, since no such course existed in our program of studies and in the curricula of most U.

I therefore consider this book, which closely follows these lecture notes, to be suitable for such undergraduate students.

Furthermore, it should be applicable to students at that level from essentially every science and engineering University department. Its prerequisites are the mathematical fundamentals in terms of some linear algebra and calculus, and computational programming skills not limited to a particular programming language that all such students possess.

Indeed, I strongly believe that Neural Networks are a eld of both intellectual interest and practical value to all such students and young professionals. Articial neural networks not only provide an understanding into an important computational architecture and methodology, but they also provide an understanding very simplied, of course of the mechanism of the biological neural network.

Developing empirical models from observational data using artificial neural networks | SpringerLink

Neural networks were until recently considered as a toy by many computer engineers and business executives. This was probably somewhat justied in the past, since neural nets could at best apply to small memories that were analyzable just as successfully by other computational tools.

I believe and I tried in the later chapters below to give some demonstration to support this belief that neural networks are indeed a valid, and presently, the only ecient tool, to deal with very large memories. The beauty of such nets is that they can allow and will in the near-future allow, for instance, a computer user to overcome slight errors in representation, in programming missing a trivial but essential command such as a period or any other symbol or character and yet have the computer execute the command. This will obviously require a neural network buer between the keyboard and the main proxiii.

It should allow browsing through the Internet with both fun and eciency.

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Advances in VLSI realizations of neural networks should allow in the coming years many concrete applications in control, communications and medical devices, including in articial limbs and organs and in neural prostheses, such as neuromuscular stimulation aids in certain paralysis situations. For me as a teacher, it was remarkable to see how students with no background in signal processing or pattern recognition could easily, a few weeks hours into the course, solve speech recognition, character identication and parameter estimation problems as in the case studies included in the text.

Such computational capabilities make it clear to me that the merit in the neural network tool is huge. In any other class, students might need to spend many more hours in performing such tasks and will spend so much more computing time. Note that my students used only PCs for these tasks for simulating all the networks concerned. Since the building blocks of neural nets are so simple, this becomes possible. And this simplicity is the main feature of neural networks: A house y does not, to the best of my knowledge, use advanced calculus to recognize a pattern food, danger , nor does its CNS computer work in picosecond-cycle times.

Researches into neural networks try, therefore, to nd out why this is so. This leads and led to neural network theory and development, and is the guiding light to be followed in this exciting eld. Chapter 3. Basic Network Structures. The Basic Structure.

ISBN 13: 9789814522731

The Single-Layer Representation Problem. The Limitations of the Single-Layer Perceptron. Many-Layer Perceptrons. Madaline Training. Madaline Case Study: Character Recognition. The Back Propagation Learning Procedure. Derivation of the BP Algorithm. Modied BP Algorithms. Hopeld Networks 7. Binary Hopeld Networks.

Walsh Functions.

Neural Networks as Cybernetic Systems – Part 2

Network Stability. Summary of the Procedure for Implementing the Hopeld Network. Continuous Hopeld Models. The Continuous Energy Lyapunov Function. Chapter 8.

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Counter Propagation 8. Grossberg Layer. Training of the Kohonen Layer. Training of Grossberg Layers. The Combined Counter Propagation Network. Chapter 9. Forgetting Feature.

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Training vs. Operational Runs. Operation in Face of Missing Data.