By Barbara Watters

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5 A Brief History and Overview of Nonclassical Theories The three main types of nonclassical adaptive or learning systems are ANN, FL and GAs. These form the foundation of what is now called the computational intelligent systems that have slowly developed into viable and accepted engineering solution methods over the past six decades. Although their origins are not much more recent than the classical adaptive filtering theories they have found broader commercial application only in more recent times.

A significant resurgence in interest in ANNs occurred in the 1980s as computers got bigger, faster and cheaper. This ubiquitous computing power allowed the development of many mathematical tools to express analytically, the complex equilibrium state energy landscapes necessary to study ANN architectures. Because of this increased and enthusiastic research activity, especially in conjunction with statistics, many new and useful learning theories have now been proposed and implemented. One of the most important of these is Vapnik’s “Statistical Learning Theory“ (Cherkassky and Mulier 1998).

However, funding and research activity in ANNs took a major dive after the publication of Minsky and Papert’s book “Perceptrons” in 1969, which was mistakenly thought to have criticised the whole field of ANNs rather than just the simple Perceptron. The decade of the 1970s saw a much reduced but stable activity in ANN research by a smaller number of researchers including Kohonen, Anderson, Grossberg and Fukushima. After the low period of the 1970s, several very 18 Principles of Adaptive Filters and Self-learning Systems significant publications appeared between 1982 and 1986 that advanced the state of ANN research.

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