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Intelligent Systems are considered to be the computing tools of Artificial Intelligence. They can be applied in the field of Data mining and work diligently in order to discover useful knowledge from data. Intelligent Systems can be classified in a number of categories depending on the mathematical models they utilize and their problem solving approach. They can also be combined together to form hybrid systems with improved properties. One way to divide them is the following:
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Artificial Neural Networks attempt to simulate in a mathematical manner the human neural system and thus, mimic the distributed operation of the brain. They are consisted of simple structural elements called neurons, which can be controlled by certain parameters and are able to "learn" and respond in a "smart" way to any new stimulus.
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Fuzzy Systems derive from the theory of Fuzzy Logic and Fuzzy Sets. This theory provides the means to design and develop systems that can operate and take decisions in ambiguous environments, much like an expert does.
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Genetic Algorithms were devised in the 60s and are commonly related to a theory known as Evolutionary Programming. The concept behind these algorithms is based on the adaptive operations performed by nature, when a population of "chromosomes" moves toward a new population through "natural selection" (according to Darwin). During this procedure, the chromosomes can reproduce, mutate and cross-breed in order to produce better and stronger ("fitter") offsprings, some of which will prevail in the end.
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