The area of Neural computing that we shall discuss in this book represents a combination of techniques of classical optimization, statistics, and information theory. Neural network was once widely called artificial neural networks, which represented how the emerging technology was related to artificial intelligence. It once was a topic that had captivated the interest of most computer scientists, engineers, and mathematicians. Its charm of being an adaptive system or universal functional approximator has compelling appeal to most researchers and engineers. The Backpropagation training algorithm was once the most popular keywords used in most engineering conferences. There is an interesting history on this area dated back from the late fifties which we saw the advent of the Mark I Perceptron. But the real intriguing history started from the sixties that we saw Minsky and Papert’s book “Perceptrons” discredited the early neural research work. For all neural researchers, the late eighties are well remembered because the research of neural networks was reinstated and repositioned. From the nineties to the new millennium is history to be made by all neural researchers. We saw the flourish of this topic and its applications stretched from rigorous mathematical proof to different physical science and even business applications. Researchers now tend to use the term “neural networks” instead of “artificial neural networks” when we have understood the theoretical background more. There have been volumes of research literature published on the new development of neural theory and applications. There have been many attempts to discuss this topic from either a very mathematical way or a very practical way. But to most users including students and engineers, how to employ an appropriate neural network learning algorithm and the selection of model for a given physical problem appear to be the main issue.

This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area. Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. It also contains several interesting IT, engineering and bioinformatics applications.