K.J. Astrom and T.J. McAvoy. Intelligent control: an overview and evaluation. In L. Boullart, A. Krijgsman, and R.A. Vingerhoeds, editors, Application of artificial intelligence in process control. Pergamon Press, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... need an exact model of the system and when the former is not available, new methods have been developed: planification (geometric approach) fuzzy and expert systems (rule based approach with structured information) neural nets (coding the knowledge into a black box with learning abilities) AM92, Can87, CL90, FT87, Lat91, Lau90, LTJ90, Lee91, Mel90, NSA90, NW90a, NW90b, vdRvNLD90] 4 Learning to control a system If we see the control of a system as a skill, we can distinguish two different parts: acquisition of the skill and refinement of the skill. These may be related to more ....

....ways of determining automatically a mapping between topologies and applications. Only very recently some progress was made in order to cope with that problem: SY93] proposes a method to determine the adequate number of fuzzy sets and fuzzy rules relatively to a set of multidimensional data. AM92] surveys a few alternative algorithms, which provide partial solutions to the determination of network structure. Under specific (and unfortunately intractable in practice) assumptions and for a class of universal neural networks, AS92a, AS92b] shows how the internal structure of the ....

K.J. Astrom and T.J. McAvoy. Intelligent control: an overview and evaluation. In L. Boullart, A. Krijgsman, and R.A. Vingerhoeds, editors, Application of artificial intelligence in process control. Pergamon Press, 1992.

....control law. A user s manual to FuzzyCAT can be found in chapter 4 and a listing of all the on line help texts is given in chapter 5. 1. 2 Basics of Fuzzy Control For an introduction to the basic concepts and methods of fuzzy control we refer to the large literature existing in this area, e.g. [1, 3, 4, 7, 15, 18]. Some reports mirroring the view of the authors are [5, 6, 7] To fix terminology we devote some paragraphs recalling the basics of f.c. From a functional point of view a fuzzy controller simply constitutes a nonlinear mapping of its input to its output signals, where the nonlinearity is given by ....

.... # The combined fuzzy output function is defuzzified and # a fuzzy control law is obtained. cm: defuzz(rm) # The unknown parameters are assigned values. emax: 2: emax: 100: b: 10: umax: 20: # The fuzzy control law is plotted. fuzzyplot(cm) SEE ALSO: datatypes[fuzzycat] REFERENCES: [1] A. Stenman FuzzyCAT ett analysverktyg for fuzzyreglering Master s thesis, LiTH ISY EX 1323, Dept. of Electrical Engineering, Linkoping University, S 581 83 Linkoping, Sweden, 1993. 2] K. Forsman, A. Stenman, J E. Stromberg FuzzyCAT Towards a Mathematical Analysis of Fuzzy Controllers ....

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K.J. Astrom and T.J. MacAvoy. Intelligent control: an overview and evaluation. In D.A. White and D.A. Sofge, editors, Handbook of Intelligent Control. Neural, Fuzzy, and Adaptive Approaches, chapter 1, pages 3--35. Van Nostrand Reinhold, 1992.

....high demands on mathematical rigor and analysis before accepting new techniques, and such mathematical analyses are still scarce in the available FC literature. Nevertheless, FC has so many advantages that its importance cannot be neglected. Some references to the basics of FC and fuzzy logic are [1, 3, 5, 7, 12, 15]. We devote this introductory section to recalling the very basic ideas of FC, since there are comparatively few transparent accounts for this in the literature and the perspective is rarely the one a control theorist would prefer. Also the numerous approaches to various FC technicalities add to ....

K.J. Astrom and T.J. MacAvoy. Intelligent control: an overview and evaluation. In D.A. White and D.A. Sofge, editors, Handbook of Intelligent Control. Neural, Fuzzy, and Adaptive Approaches, chapter 1, pages 3--35. Van Nostrand Reinhold, 1992.

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