Book123

    Free register and download UseNet downloader, then you can FREE Download from UseNet.

    Download without Limit " Study Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) " from UseNet for FREE!

• Author: Vojislav KECMAN
• Publisher: The MIT Press
• Publish Date: March 19, 2001
• ISBN: 0262112558
• Pages: 608

• Learning from experimental data (examples, samples, measurements, records, patterns, or observations) by support vector machines (SVMs) and neural networks (NNs)
• Embedding existing structured human knowledge (experience, expertise, heuristics) into workable mathematics by fuzzy logic models (FLMs).

These problems seem to be very different, and in practice that may well be the case. However, after NN or SVM modeling from experimental data is complete, and after the knowledge transfer into an FLM is finished, these two models are mathematically very similar or even equivalent. This equivalence, discussed in section 6.2, is a very attractive property, and it may well be used to the benefit of, both fields.

The need for a book about these topics is clear. Recently, many new 'intelligent' products (theoretical approaches, software and hardware solutions, concepts, devices, systems, and so on) have been launched on the market. Much effort has been made at universities and R&D departments around the world, and numerous papers have been written on how to apply NNs, FLMs, and SVMs, and the related ideas of learning from data and embedding structured human knowledge. These two concepts and associated algorithms form the new field of soft computing. They have been recognized as attractive alternatives to the standard, well established 'hard computing' paradigms. Traditional hard computing methods are often too cumbersome for today's problems. They always require a precisely stated analytical model and often a lot of computation time. Soft computing techniques, which emphasize gains in understanding system behavior in exchange for unnecessary precision, have proved to be important practical tools for many contemporary problems. Because they are universal approximators of any multivariate function, NNs, FLMs, and SVMs are of particular interest for modeling highly nonlinear, unknown, or partially known complex systems, plants, or processes. Many promising results have been reported. The whole field is developing rapidly, and it is still in its initial, exciting phase.

At the very beginning, it should be stated clearly that there are times when there is no need for these two novel model-building techniques. Whenever there is an analytical closed form model, using a reasonable number of equations, that can solve the given problem in a reasonable time, at reasonable cost, and with reasonable accuracy, there is no need to resort to learning from experimental data or fuzzy logic modeling. Today, however, these two approaches are vital tools when at least one of those criteria is not fulfilled. There are many such instances in contemporary science and engineering.

The title of the book gives only a partial description of the subject, mainly because the meaning of learning is variable and indeterminate. Similarly, the meaning of soft computing can change quickly and unpredictably. Usually, learning means acquiring knowledge about a previously unknown or little known system or concept. Adding that the knowledge will be acquired from experimental data yields the phrase statistical learning. Very often, the devices and algorithms that can learn from data are characterized as intelligent. The author wants to be cautious by stating that learning is only a part of intelligence, and no definition of intelligence is given here. This issue used to be, and still is, addressed by many other disciplines (notably neuroscience, biology, psychology, and philosophy). However, staying firmly in the engineering and science domain, a few comments on the terms intelligent systems or smart machines are now in order.

Without any doubt the human mental faculties of learning, generalizing, memorizing, and predicting should be the foundation of any intelligent artificial device or smart system. Many products incorporating NNs, SVMs, and FLMs already exhibit these properties. Yet we are still far away from achieving anything similar to human intelligence. Part of a machine's intelligence in the future should be an ability to cope with a large amount of noisy data coming simultaneously from different sensors. Intelligent devices and systems will also have to be able to plan under large uncertainties, to set the hierarchy of priorities, and to coordinate many different tasks simultaneously. In addition, the duties of smart machines will include the detection or early diagnosis of faults, in order to leave enough time for reconfiguration of strategies, maintenance, or repair. These tasks will be only a small part of the smart decision making capabilities of the next generation of intelligent machines. It is certain that the techniques presented here will be an integral part of these future intelligent systems.