"Expert Systems"

flighty intercommunicates, on the former(a) hand, attempt to mimic the croping of the human nous, although on a much smaller scale. This eliminates the need for consulting an expert, and development time is greatly reduced (to weeks or months) (Caudill, 1991, p. 116). Neural meshworks do not blaspheme on a set of rules that are based on individual interpretation, scarce use binary processing to pull through at decisions. The problem is determining at what point a neuronal network is appropriate, and at what point an expert remains should be used.

The basic difference between expert systems and unquiet networks is that expert systems use analytical abstraction, while neural networks commit on binary processing. Developers of neural networks learned that biological neurons, quasi(prenominal) to computer bits, are either on or off. Neural networks try to reproduce the biological network electronically to raspy the abilities of the brain and nervous system to recognize patterns, satisfy constraints and process signals. The biological brain consist of 100 billion neurons, separately connected to 10,000 others by synapses; neural networks are subject to size constraints and can thus be constructed for limited applications, at this point.

notwithstanding the constraints, neural networks are constructed in such a musical mode that they correspond approximately to the way that people learn. A network is built

There is no wizard strategy that can determine whether an expert system or a neural network is the ideal choice for a system where artificial intelligence is indicated. Expert systems have agelong development cycles, but offer the advantage of having the logic undersurface their decisions easily understood by their users. Neural networks work on pattern recognition and are therefore unable to try such explanatory background, although some neural networks can be used to trace their decisions backward to develop rationalizations after the detail (Caudill, 1991, p. 114).

In many situations, the optimum solution is a hybridizing of expert system and neural network, and future systems are apt(predicate) to combine traditional computer processing with these systems to take dependable advantage of quantitative and analytical processing.

Expert systems also engage a great deal of time to develop (more than a year in many cases), while neural network systems can be developed in weeks or months. In both the case of expert systems and neural networks, they remain bound by size. Large systems of either type are save inconvenient, and must be carefully designed and maintained. Large neural networks are generally comprised of smaller networks connected in a hierarchical manner. Hardware can also be an go forth: expert systems work well on digital computers, but neural networks require accelerated or parallel hightail it boards (Caudill, 1991, p. 110).

This pattern recognition, however, is proving remarkably similar to how humans process information. It is known, for example, that interpretation is based on pattern recognition. Readers recognize word constellation more than individual letters, which is why some typefaces are harder to realize than others, and why words printed in all capitals are gruelling to read. When word shape is obscured, reading comprehension declines. The human brain recognizes the patterns of the words and interprets their meaning based on word shape, article of faith structure and gra
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