By Spyros Tzafestas, Henk Verbruggen (auth.), Professor Spyros G. Tzafestas, Henk B. Verbruggen (eds.)
This publication is anxious with synthetic Intelligence (AI) recommendations and strategies as utilized to business choice making, regulate and automation difficulties. the sphere of AI has been extended drastically over the past years as a result of that reliable theoretical and alertness effects have gathered. through the first level of AI improvement so much employees within the box have been content material with illustrations displaying principles at paintings on uncomplicated difficulties. Later, because the box matured, emphasis was once became to demonstrations that confirmed the aptitude of AI strategies to deal with difficulties of sensible worth. Now, we arrived on the degree the place researchers and practitioners are literally development AI platforms that face real-world and business difficulties. This quantity presents a collection of twenty 4 well-selected contributions that care for the appliance of AI to such real-life and business difficulties. those contributions are grouped and offered in 5 elements as follows: half 1: basic matters half 2: clever structures half three: Neural Networks in Modelling, keep watch over and Scheduling half four: procedure Diagnostics half five: business robot, production and Organizational platforms half 1 consists of 4 chapters delivering historical past fabric and working with normal matters similar to the conceptual integration of qualitative and quantitative types, the therapy of timing difficulties at method integration, and the research of right reasoning in interactive man-robot systems.
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Extra info for Artificial Intelligence in Industrial Decision Making, Control and Automation
Robustness. The network can produce reasonable outputs when incomplete data are provided or a part of the network does not function adequately. These features make it challenging to apply artificial neural networks in control engineering applications. Narendra and Parthasarathy  proposed many control schemes. These schemes can be used as a basis for an ANN-based control and identification scheme for practical applications (den Hertog, ). 4. ARTIFICIAL INTELLIGENCE IN DECISION MAKING Many attemps have been made for applying AI concepts and techniques in decision analysis (DA) models and for producing combined AI-AD systems possessing key features of both of them [40-52].
We now come at the Supervision code which is the major part of the coding of a controller outnumbering the code for the actual control algorithm several times. Supervision code is easily expressed in logic and is a natural candidate for use in an expert system. By the merging of PLC and DDC, the combination of continuous time control and discrete logic and sequencing, expert systems can be used as an alternative or an extension to logic and sequencing and can be intermingled with logic surrounding the DDC algorithms.
They produced a Prolog-based expert system shell (called ARTIFACI') for the automation of the decision and control process. Work in this direction was also done in  by Clocksin and Morgan. A more efficient control rules set for the liquid control problem derived from qualitative considerations was presented by Abdulmajid and Wynne . The controller derived (as all the above qualitative controllers) was applied at plant level and it can be considered as a replacement of the conventional controllers/compensators (say of the PID type).
Artificial Intelligence in Industrial Decision Making, Control and Automation by Spyros Tzafestas, Henk Verbruggen (auth.), Professor Spyros G. Tzafestas, Henk B. Verbruggen (eds.)