Advances in Intelligent Systems

Intelligent Systems can be defined as systems whose design, mainly based on computational techniques, is supported, in some parts, by operations and processing skills inspired by human reasoning and behaviour. Intelligent Systems must typically operate in a scenario in which non-linearities are the rule not a disturbing effect to be corrected. Finally, Intelligent Systems also have to incorporate advanced sensory technology in order to simplify man-machine interactions. Several algorithms are currently the ordinary tools of Intelligent Systems. All innovative computing techniques evolve and increase in importance independently but have as a common background the idea that exact as well as precise solutions are often not the response to practical requirements. A neologism was coined which embodies the above mentioned different techniques and approaches: Soft Computing. The guiding principle of Soft Computing is to exploit the tolerance to imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. This is strongly in contrast with the claims of Artificial Intelligence, which has based its popularity as well as its decline, on manipulation of symbols, decision trees and hard logic. Unfortunately, the most important scientific problems are ill-posed, lack of data strong, they do not often have a unique solution, and in some cases, an important information is related to the possibility of managing different solutions with different degrees of reliability. Actual signal processing calls for coping with conflicts which commonly inhibit further actions during numerical computation. Conflicts paralyse actions and thus must be avoided in standardnumerical computation. In this sense, Intelligent Systems are pervasive. This book contains a selection of contributions regarding Intelligent Systems by experts in diverse fields. Topics discussed in the book are: Applications of Intelligent Systems in Modelling and Prediction of Environmental Changes, Cellular Neural Networks for NonLinear Filtering, NNs for Signal Processing, Image Processing, Transportation Intelligent Systems, Intelligent Techniques in Power Electronics, Applications in Medicine and Surgery, Hardware Implementation and Learning of NNs.