Artificial Intelligence in Real-Time Control 1994. A by A. Crespo

By A. Crespo

Synthetic Intelligence is among the new applied sciences that has contributed to the profitable improvement and implementation of strong and pleasant keep an eye on platforms. those structures are extra appealing to end-users shortening the space among regulate idea purposes. The IFAC Symposia on synthetic Intelligence in actual Time regulate offers the discussion board to interchange rules and effects one of the top researchers and practitioners within the box. This booklet brings jointly the papers provided on the newest within the sequence and gives a key overview of current and destiny advancements of man-made Intelligence in actual Time keep watch over method applied sciences

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Additional info for Artificial Intelligence in Real-Time Control 1994. A Postprint Volume from the IFAC Symposium, Valencia, Spain, 3–5 October 1994

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Program verification is labour and time consuming, the reward is a program with certain proven properties. 2 Timeliness and Quality of the Solution In the conventional AI domain the basic criterion for assessing a problem solving method is the quality (logical and quantitative correctness) of the obtained solution. Usually the quality of a solution may be considered as a vector of several components (precision (or truth probability), risk minimization ability, cost-effectiveness, etc). In real-time applications of AI methods the timeliness component is introduced into the quality criterion and obtains special importance.

A process and the task of monitoring and optimization can be depicted schematically in the manner of Fig. 2, where the initial system state and the process parameter settings are shown as inputs to a mapping for which we know the final results. A computational description of the process is learned from a set of associated input/output pairs of observations and the process can be optimized in the sense that the inputs can be adjusted until the output is optimal. Such a view of the monitoring and optimization task is indeed valid and is often practical in that manner.

1988). Nonlinear Regression Analysis and its Application, John Wiley, N. Y. 29 Bezdek, J. C. (1992). On the relationship between neural networks, pattern recognition and intelligence, Int. J. Approximate Reasoning, vol. 6, pp. 85-107 Cybenco, G. (1989). Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals and Systems, vol. 2, pp. 303-314 Funahashi (1989). On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192. , Multilayer feedforward networks are universal approximators, Neural NetworL·, vol..

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