Hostname: page-component-7479d7b7d-qlrfm Total loading time: 0 Render date: 2024-07-11T06:03:31.715Z Has data issue: false hasContentIssue false
  • English
  • Français

Fuzzy systems and fuzzy expert control: An overview

Published online by Cambridge University Press:  07 July 2009

Spyros G. Tzafestas
Spyros G. Tzafestas
Affiliation:
Intellignet Robotics and Control Unit (IRCU), Department of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents an overview of fuzzy set theory and its application to the analysis and design of fuzzy expert control systems. Starting with a short account of the basic concepts and properties of fuzzy sets and fuzzy reasoning, a few fuzzy rule-based controllers, viz, basic single-input singleoutput fuzzy control, self-organizing fuzzy control, fuzzy PID supervisor, and the fuzzy PID incremental controller, are described in some detail. Then a survey of the theoretical results and applications is provided which gives a good picture of the current status of the field. This survey includes the work on neuro-fuzzy systems, and software systems for the representation and processing of fuzzy information. The paper closes with four application examples which show the type of results that must be expected from fuzzy expert control.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 9 , Issue 3 , September 1994 , pp. 229 - 268
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adamo, JM, 1980a. “L.P. L. A fuzzy programming language: 1 Syntactic Aspects”. Fuzzy Sets and Systems 3 151–179.CrossRefGoogle Scholar
Adamo, JM, 1980b. “L.P.L. A fuzzy programming language: 2. Semantic Aspects” Fuzzy Sets and Systems 3 261–289.CrossRefGoogle Scholar
Aliev, RA, Oulanov, GM and Tserkovnyi, AE, 1986. “Decision making in intelligent robots”. Soviet Physics Doklady 31 (10) 780–782.Google Scholar
Amano, A and Aritsuka, T, 1989. “On the use of neural networks and fuzzy logic in speech recognition”. In: Proc. hit. Joint Conf. on Neural Networks Washington, DC, pp. 301–305.Google Scholar
Bardossy, A, 1990. “Note on fuzzy regression”. Fuzzy Sets and Systems 37 65–75.CrossRefGoogle Scholar
Bartolini, G, Casolino, G, Daoli, F and Mortem, M, 1985. “Development of performance adaptive fuzzy controllers with application to continuous casting plans”. In: IndustrialApplications of Fuzzy Control (Sugeno, M., ed.). North-Holland, pp. 73–86.Google Scholar
Bellman, R and Giertz, M, 1973. “On the analytic formalism of the theory of fuzzy sets”. Information Science 5 149–156.CrossRefGoogle Scholar
Berenji, HR, 1992. “Fuzzy and neural control”. In: An introduction to intelligent and Autonomous Control (Antsaklis, P. and Passino, K., eds.). Kluwer.Google Scholar
Bertsekas, DP, 1971. “Control of uncertain systems with a set-membership description of the uncertainty”. MIT Electr. Syst. Lab. Rept ESL-R-447, Cambridge, MA.Google Scholar
Black, M, 1963. “Reasoning with loose concepts”. Dialogue 2 1–12.CrossRefGoogle Scholar
Bounas, A, 1993. “Criteria for fuzzy reasoning through relations for conditional propositions”. The Journal of Fuzzy Mathematics 1 (3) 587–591.Google Scholar
Bounas, A and King, RE, 1994. “Criteria for value approximations of fuzzy systems”. Tech. Report, NRC “Demokritos”, Aghia Paraskevi, Attiki, Greece.Google Scholar
Bounds, DG, Lloyd, PJ and Mathew, BG, 1990. “A comparison of neural network and other pattern recognition approaches to the diagnosis of low back disorders”. Neural Networks 3 583–591.CrossRefGoogle Scholar
Brace, M and Rutherford, DA, 1979. “Theoretical and linguistic aspects of the fuzzy logic controller”. Automatica 15 553–577.Google Scholar
Brown, M, Fraser, R, Harris, CJ and Moore, CG, 1991. “Intelligent self-organizing control for autonomous guided vehicle: comparative aspects of fuzzy logic and neural nets”. In: '91 lEE international Conference on Control, pp. 134–139.Google Scholar
Buckley, JJ and Siler, W, 1987. “Managing uncertainty in a fuzzy expert system. Part I: Combining uncertainties”.Preprints: 2nd IFSA Congress, pp. 737–739.Google Scholar
Cassinis, R, Biorli, E, Meregalli, A and Scalise, F, 1988. “Behavioral model architecture: a new way of doing real-time planning in intelligent robots”. International Society for Optical Engineering 852 275–280.Google Scholar
Celmins, A, 1987. “Least squares model fitting to fuzzy linear data”. Fuzzy Sets and Systems 22 245–269.CrossRefGoogle Scholar
Chang, SSL and Zadeh, LA, 1972. “On fuzzy mapping and control”. IEEE Trans. Sys. Man Cybern 2 30–34.CrossRefGoogle Scholar
Cohen, ME and Hudson, DL, 1992. “Approaches to the handling of fuzzy input data in neural networks”. In: Proc. 1st IEEE Conf. on Fuzzy Systems San Diego, CA. pp. 93–100.Google Scholar
Deepak, S, Repko, MC, Mood, NC and Kelley, RB, 1989. “A fuzzy approach to the interpretation of robot assembly forces”. International Society for Optical Engineering 1002 426–433.Google Scholar
De Silva, CW and McFarlane, AG, 1989. “Knowledge-based control approach for robotic manipulators”. International Journal of Control 50 (1) 249–273.CrossRefGoogle Scholar
Deyong, R, Poison, J, Moore, R, Weng, C and Lara, J, 1992. “Fuzzy and adaptive control simulation for a walking machine”. IEEE Control Systems Magazine 12 (3) 43–50.Google Scholar
Di Nola, A, Pedrycz, W and Sessa, S, 1985. “On measures of fuzziness of solutions of fuzzy relation equations with generalized connectives”. J. Math. Anal. and Appl 106 443–453.CrossRefGoogle Scholar
Diamond, P, 1990. “Higher-level fuzzy numbers arising from fuzzy regression models”. Fuzzy Sets and Systems 36 265–275.CrossRefGoogle Scholar
Dodds, DR, 1988a. “Fractals, fuzzy sets and image representation”. International Society for Optical Engineering 1001 87–94.Google Scholar
Dodds, DR, 1988b. “Fuzziness in knowledge-based robotic systems”. Fuzzy Sets and Systems 26 (2) 179–193.CrossRefGoogle Scholar
Dote, Y, Suyitno, A and Strefezza, M, 1990. “Fuzzy learning grasping force controller for manipulator hand”.In: Proc. IECON '90: 16th Annual Conference of IEEE industrial Electronics Society, pp. 1259–1265.CrossRefGoogle Scholar
Dubois, D and Pradé, H, 1978. “Operations on fuzzy numbers”. Int. J. Systems Sci 9 (6) 613–626.CrossRefGoogle Scholar
Fukami, S, Mizumoto, M and Tanaka, K, 1980. “Some considerations of fuzzy conditional inference”. Fuzzy Sets and Systems 4 243–273.CrossRefGoogle Scholar
Fukuda, T, Fujiyoshi, T, Arai, F and Matsuura, H, 1991. “Design and dextrous control of micromanipulator with 6 dof”. In: Proc. IEEE international Conference on Robotics and Automation, pp. 1628–1633.CrossRefGoogle Scholar
Gain, B, 1983. “Precise past–fuzzy future”. Int. J. Man-Machine Studies 19 117–134.Google Scholar
Garcia-Cerczo, A, Barreiro, A and Ollero, A, 1991. “Design of robust intelligent control of manipulators”. In: Proc. IEEE International Conference on Systems Engineering, pp. 225–228.CrossRefGoogle Scholar
Gasos, J, Garcia-Allegre, MC and Garcia, Rosa R, 1990. “Fuzzy local navigation of a simulated real robot in unknown environment”. In: Proc. IEEE International Workshop on Intelligent Motion Control, pp. 445–449.Google Scholar
Gorcz, R and De Neyer, M, 1994. “Fuzzy control of robotic manipulators and mechanical systems. In: Fuzzy Reasoning in Information and Control Systems (Tzafestas, SG and Venetsanopoulos, AN, eds.). Kluwer, pp. 451–491.Google Scholar
Gupta, MM, 1991. “Theory of T-norms and fuzzy inference methods”. Fuzzy Sets and Systems 40 431–450.CrossRefGoogle Scholar
Hai, Quan Dai, Dalton, GR and Tulenko, J, 1992. “Fuzzy control system for a mobile robot”. Trans. of American Nuclear Society 65 464–465.Google Scholar
Han-Gyoo, KA, 1991. “A new interpretation of force&servo control of robot arms”. In: Proc IROS '91 International Workshop on Intelligent Robots and Systems, pp. 1623–1627.Google Scholar
Harris, CJ and Brown, M, 1991. “Intelligent control for autonomous guided vehicles”. Proc. IEEE Colloquium on Intelligent Control 3 1–4.Google Scholar
Higashi, M, Di, Nola A, Pedrycz, W and Sessa, S, 1984. “Ordering fuzzy sets by consensus concept and fuzzy relation equations”. Int. J. General Syst. 10 47–56.CrossRefGoogle Scholar
Hirota, K, 1979. “Extended fuzzy expressions of probabilistic sets”. In: Advances in Fuzzy Set Theory and Applications (Gupta, MM, Ragade, RK and Yager, RR, eds.). North Holland, pp. 201–214.Google Scholar
Hirota, K, 1981. “Concepts of probabilistic sets”. Fuzzy Sets and Systems 5 31–46.CrossRefGoogle Scholar
Hirota, K, Arai, Y and Hachisu, S, 1986. “Moving mark recognition and moving object manipulation in fuzzy controlled robot”. Control-Theory and Advanced Technology 2 399–418.Google Scholar
Hirota, K, Arai, Y and Hachisu, S, 1989. “Fuzzy control robot arm playing two-dimensional ping-pong game”. Fuzzy Sets and Systems 32 (2) 149–159.CrossRefGoogle Scholar
Holt, AW and Commoner, F, 1970. “Events and conditions”. New York: Applied Data Research. (Also in Rec. Project MAC Conf. Concurrent Systems and Parallel Computation New York, ACM, pp. 1–72.)Google Scholar
lschibuchi, H, Okada, H and Tanaka, H, 1992. “Interpolation of fuzzy If-Then rules by neural networks”. In: Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 337–340.Google Scholar
lshii, K, and Misra, S, 1990. “Uncertainty management in intelligent task planning of mobile robots”. lnternational Journal of Computer Integrated Manufacturing 3 (1) 13–23.Google Scholar
Ishikawa, S, 1991. “A method of indoor mobile robot navigation by using fuzzy control”. In: Proc. IROS '91 International Workshop on Intelligent Robots and Systems, pp. 1013–1018.Google Scholar
Isik, C, 1988. “Inference engines for fuzzy rule-based control”. International Journal of Approximate Reasoning 2 (2) 178–188.CrossRefGoogle Scholar
Isik, C and Meystel, AM, 1988. “Pilot level of a hierarchical controller for an unmanned mobile robot”. IEEE Journal of Robotics and Automation 4 (3) 241–255.CrossRefGoogle Scholar
Jajuga, K, 1986. “Linear fuzzy regression”. Fuzzy Sets and Systems 20 343–353.CrossRefGoogle Scholar
Janko, WH, Roubens, M and Zimmermann, H, 1990. “Progress in Fuzzy Sets and Systems”. Kiuwer.CrossRefGoogle Scholar
Jensen, JH, 1979. “Fuzzy logic control of industrial process”. PhD Thesis, Electr. Power Eng. Dept., Tech. Univ. of Denmark.Google Scholar
Joszef, S. 1992. “On the effect of linear data transformations in possibilistic fuzzy linear regression”. Fuzzy Sets and Systems 45 185–188.Google Scholar
Kalley, GS and McDermott, KJ, 1989. “Using expert systems to incorporate the principles of motion economy as a means of improving robot manipulator path generation”. Computers and Industrial Engineering 16 (32) 207–213.CrossRefGoogle Scholar
Kang, H and Vachtsevanos, G, 1990. “An intelligent strategy to robot coordination and control”. In: Proc. CDC '90: 29th IEEE Conference on Decision and Control pp. 2208–2213.CrossRefGoogle Scholar
Kawarnura, H, Tani, A, Yakamoto, K and Yamada, M, 1990. “Constitution of intelligent fuzzy network by frame knowledge representation”. In: Proc. mt. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 261–265.Google Scholar
Kawamura, H, Tani, A, Kambara, H and Yamada, M, 1991. “Intelligent fuzzy network for optimum structural planning and design”. In: Proc. 7th Fuzzy Systems Symp. Nagoya, Japan, pp. 99–102.Google Scholar
Kawamura, H, Tani, A and Kambara, H, 1992. “A seismic structural planning system for fuzzy network”. In: Proc. 10th World Conf. on Earthquake Engineering Madrid, Spain, pp. 6271–6275.Google Scholar
Keller, JM, Yager, RR and Tahani, H, 1992. “Neural network implementation of fuzzy logic”. Fuzzy Sets and Systems 45 1–12.CrossRefGoogle Scholar
Keller, JM and Tahani, H, 1992. “Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks”. Int. J. Approximnate Reasoning 6 221–240.CrossRefGoogle Scholar
Kemal, C, 1989. “Fuzzy rule-based motion controller for an autonomous mobile robot”. Robotica 7 (1) 37–42.Google Scholar
Kickert, W and Van Nauta Lemke, HR, 1976. “The application of fuzzy set theory to controller in warm water plant”. Automnatica 12 301–308.CrossRefGoogle Scholar
Kickert, WJM and Mamdani, EH, 1978. “Analysis of a fuzzy logic controller”. Fuzzy Sets and Systems 1 29–44.CrossRefGoogle Scholar
Kickert, WJM, 1978. “Fuzzy Theories on Decision Making”. Martinus Nijhoff.Google Scholar
King, RE, 1986. “Expert systems in the cement industry”. Zement Kalk. Gips, 39 (3).Google Scholar
King, PJ and Mamdani, EH, 1977. “The application of fuzzy control systems to industrial processes”. In: Proc 6th IFAC World Congress (Boston 1975). Automatica 13 235–242.CrossRefGoogle Scholar
King, RE and Karonis, FC, 1986. “Rule-based systems in the process industry”. In: Proc. 25th IEEE CDC Athens.CrossRefGoogle Scholar
Klir, GJ and Folger, TA, 1988. Fuzzy Sets, Uncertainty and Information. Prentice Hall.Google Scholar
Koh, KC, Cho, AHS, Kim, SK and Jeong, Is, 1990. “Application of self-organizing fuzzy control to the joint control of a Puma 760 robot”. In: Proc. IROS '91 International Workshop on Intelligent Robots and Systems pp. 537–542.Google Scholar
Kosko, B, 1991. Neural Networks and Fuzzy Systems: A dynamical system approach to maclime intelligence. Prentice Hall.Google Scholar
Kubota, TK and Hashimoto, H, 1990. “A strategy for collision avoidance among moving obstacles for a mobile robot”. In: Proc. 11th IFAC World Congress on Automatic Control in the Service of Mankind, pp. 105–110.CrossRefGoogle Scholar
Kuncicky, DC and Kandel, A, 1989. “A fuzzy interpretation of neural networks”. In: Proc. 3rd Int. Fuzzy Systems Association Congress Seattle, WA, pp. 113–116.Google Scholar
Kwok, DP, Tam, P, Li, CK and Wang, P, 1990. “Linguistic PID controllers”. In: Proc. 11th IFAC World Congress on Automatic Control in the Service of Mankind Tallin, Estonia, pp. 192–197.Google Scholar
Lakov, D, 1985. “Adaptive robot under fuzzy control”. Fuzzy Sets and Systems 17 (1) 1–8.CrossRefGoogle Scholar
Larsen, PM, 1980. “Industrial applications of fuzzy logic control”. Int. J. Man-Machine Studies 12 3–10.CrossRefGoogle Scholar
Lee, CC, 1990. “Fuzzy logic in control systems: fuzzy logic controller (Part I)”. IEEE Trans. Syst. Man Cybern. 20 (2).Google Scholar
Leung, KS and Lam, W, 1988. “Fuzzy concepts in expert systems”. Computer Magazine IEEE 21 (9) 43–58.CrossRefGoogle Scholar
Li, Y, 1986. “The automatic recognition of mechanical parts on membership's principles of fuzzy sets”. International Society for Optical Engineering 697 175–180.Google Scholar
Li, YF and Lau, CC, 1989. “Development of fuzzy algorithms for servo systems”. IEEE Control Systems Magazine 9 (3) 65–71.CrossRefGoogle Scholar
Lim, CM and Hiyama, TI, 1991. “Application of fuzzy logic control to manipulator”. IEEE Trans. on Robotics and Automation 7 (5) 688–691.CrossRefGoogle Scholar
Lin, CE and Sheu, YR, 1992. “A hybrid control-approach for pendulum-car control”. IEEE Trans. on Industrial Electronics 39 (3) 208–214.CrossRefGoogle Scholar
Looney, CG, 1994. “Fuzzy Petri nets and applications”. In: Fuzzy Reasoning in Information, Decision and Control Systems (Tzafestas, SG and Venetsanopoulos, AN, eds.). Kluwer, pp. 511–527.CrossRefGoogle Scholar
Looney, CG, 1988a. “Expert control design with fuzzy rule matrices”. Int. J. Expert Systems 1 (2) 159–165.Google Scholar
Looney, CG, 1988b. “Fuzzy Petri nets for rule based decision making”. IEEE Trans. Syst. Man and Cybern. 18 (1) 178–183.CrossRefGoogle Scholar
MacVicar-Whelan, PJ, 1975. “Fuzzy sets, the concept of height and hedge”. Tech. Memorandum 1, Physics Department, Grand Valley State Colleges, Allendale, MI.Google Scholar
MacVicar-Whelan, PJ, 1976. “Fuzzy sets for man-machine interaction”. Int. J. Man-Machine Studies 8 687–697.CrossRefGoogle Scholar
Mamdani, EH and Assilian, S, 1974. “An experiment in linguistic synthesis with a fuzzy logic controller”. Int. J. Man-Machine Studies 7 1–13.CrossRefGoogle Scholar
Mamdani, EH and Assilian, S, 1975. “A fuzzy logic controller for a dynamic plant”. Int. J. Man-Machine Studies 7 1–13.CrossRefGoogle Scholar
Mamdani, EH, 1976.. “Advances in the linguistic synthesis of fuzzy controllers”. Int. I. Man-Machine Studies 8 669–678.CrossRefGoogle Scholar
Mamdani, EH, 1977. “Application of fuzzy logic to approximate reasoning using linguistic synthesis”. IEEE Trans. Computers 26 1182–1191.CrossRefGoogle Scholar
Mamdani, EH and Procyk, TJ, 1979. “A linguistic self-organizing process controller”. Automatica 15 15–30.Google Scholar
Mandič, NJ, Scharf, EM and Mamdani, EH, 1985. “Practical application of a heuristic fuzzy rule-based controller to the dynamic control of a robot arm”. Proc. IEEE 132 (D) 190–203.CrossRefGoogle Scholar
Martin, TP, Baldwin, JF and Pilsworth, BW, 1987. “The implementation of FPROLOG–A fuzzy prolog interpreter”. Fuzzy Sets and Systems 23 119–129.CrossRefGoogle Scholar
Masuoka, R, Watanabe, N, Kawamura, A, Owada, Y and Asakawa, A, 1990. “Neurofuzzy system-fuzzy inference using structured neural network”. In: Proc. 1990 Int. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 173–177.Google Scholar
Miyakoshi, M and Shimbo, M, 1985. “&utions of composite fuzzy relational equations with triangular norms”. Fuzzy Sets and Systems 16 53–63.CrossRefGoogle Scholar
Miyakoshi, M and Shimbo, M, 1986. “Lower &utions of systems of fuzzy equations”. Fuzzy Sets and Systems 19 37–46.CrossRefGoogle Scholar
Miyarnoto, S, 1990. Fuzzy Sets in information Retrieval and Cluster Analysis. Kluwer.Google Scholar
Mizumoto, M and Tanaka, K, 1976. “Some properties of fuzzy sets of type 2”. Information and Control 31 312–340.CrossRefGoogle Scholar
Mizurnoto, M, Fukami, S and Tanaka, K, 1979. “Some Methods of fuzzy reasoning”. In: Advances in Fuzzy Set Theory and Applications (Gupta, MM, Ragade, RK and Yager, RR, eds.) North-Holland, pp. 117–126.Google Scholar
Mizumoto, M and Zimmermann, H, 1982. “Comparison of fuzzy reasoning methods”. Fuzzy Sets and Systems 8 253–283.CrossRefGoogle Scholar
Mizumoto, M, 1985. “Extended fuzzy reasoning”. In: Approximate Reasoning in Expert Systems (Gupta, M et at., eds.). North-Holland, pp. 71–85.Google Scholar
Mizumoto, M, 1988. “Fuzzy controls under various fuzzy reasoning methods”. Information Sciences 45 129–151.CrossRefGoogle Scholar
Muarakami, S, Takemoto, F, Fujimura, H and Ide, E, 1989. “Weld-line tracking control of arc welding robot using fuzzy logic controlled”. Fuzzy Sets and Systems 32(4) 221–237.CrossRefGoogle Scholar
Murata, T, Subrahmanian, VS and Wakayama, T, 1991. “A Petri net model for reasoning in the presence of inconsistency”. IEEE Trans. Knowledge and Data Engineering 3(3) 281–292.CrossRefGoogle Scholar
Nakanishi, S, Tagaki, T, Uehara, K and Gotoh, Y, 1990. “Self-organizing fuzzy controllers by neural networks”. In: Proc. tnt. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 187–192.Google Scholar
Nakanishi, S and Takagi, T, 1990. “Pattern recognition by neural networks and fuzzy inference”. In: Proc. 1990 mt. Conf. on Fuzzy Logic and Neural Networks, lizuka, Japan, pp. 183–186.Google Scholar
Nedungadi, A and Wenzel, DJ, 1991. “A novel approach to robot control using fuzzy logic”. In: Proc. '91 IEEE international Conference on Systems, Man and cybernetics pp. 1925–1930.Google Scholar
Ncgoita, CV and Ralescu, D, 1975. Application of Fuzzy Sets to Systems Analysis. Birkhauser Verlag.CrossRefGoogle Scholar
Negoita, CV and Ralescu, DA, 1987. Simulation, Knowledge-based Computing and Fuzzy Statistics. Van Nostrand Reinhold.Google Scholar
Okada, H., Watanabe, N, Kawamura, A, Asakawa, A, Taira, T, Ishida, K, Kaji, T and Narita, N, 1992. “Knowledge implementation multilaycr neural networks with fuzzy logic”. In: Proc. 2nd int. Conf. on Fuzzy Logic and Neural Networks, lizuka, Japan, pp. 99–102.Google Scholar
Ostergaard, JJ, 1976. “Fuzzy logic control of a heat exchange process”. Report No. 7601, Electr. Power Dept. Tech. Univ. of Denmark.Google Scholar
Palm, R, 1989. “Fuzzy controller for a sensor guided robot manipulator”. Fuzzy Sets and Systems 31(2) 133–149.CrossRefGoogle Scholar
Pao, YH, 1989. Adaptive Pattern Recognition and Neural Networks. Addison-Wesley.Google Scholar
Pappis, CP and Sugeno, M, 1976. “Fuzzy relational equations and the inverse problem”. Internal Report. Queen Mary College, London.Google Scholar
Pappis, CP and Sugeno, M, 1985. “Fuzzy relational equations and the inverse problem”. Fuzzy Sets and System 15 79–90.CrossRefGoogle Scholar
Pcrdrycz, W, 1980. “On the use of Lukasiewicz logic for fuzzy control”. Arch. Autoin. 3 301–313.Google Scholar
Perdrycz, W, 1993. Fuzzy Control and Fuzzy Systems. Wiley.Google Scholar
Peterson, JL, 1981. Petri Net Theory and the Modelling of Systems. Prentice-Hall.Google Scholar
Petri, CA, 1962. “Kommunikation mit automaten”. Schriften des Rheinish-Westfalischen Institute für lnstrumcntelle Mathematik an der Universität Bonn Heft 2, Bonn, Germany.Google Scholar
Reising, W, 1985. Petri Nets. Springer-Verlag.CrossRefGoogle Scholar
Sakawa, M, 1983. “Interactive computer programs for fuzzy linear programming with multiple objectives”. bit. J. Man-Machine Studies 18 489–503.CrossRefGoogle Scholar
Sakawa, M and Yano, H, 1986. “An interactive fuzzy decision making method using constraint problems”. IEEE Trans. Systems Man cybern. 16 179–182.CrossRefGoogle Scholar
Scharf, EM and Mandi, NJ, 1985. “application of a fuzzy controller to the control of a multi-degree of freedom robot arm”. In: Industrial Applications of Fuzzy Control (Sugeno, M, ed.) North-Holland.Google Scholar
Scharf, E, 1985. “Fuzzy logic could redefine robot control”. Automation 21(2) 11–14.Google Scholar
Schweppe, FC, 1968. “Recursive state estimation: Unknown but bounded errors and systems inputs”. IEEE Trans. Auto. Contr. 13(1) 22–28.CrossRefGoogle Scholar
Scung-Woo, K and Mignon, P, 1991. “Fuzzy compliance robot control”. In: Proc. IROS '91 International Workshop on intelligent Robots and Systems pp. 1628–1631.Google Scholar
Siler, W, 1987. “FLOPS: A fuzzy expert system shell”. Preprints: 2nd IFSA Congress pp. 848–850.Google Scholar
Siler, W and Buckley, JJ, 1987. “Managing uncertainty in a fuzzy expert system. Part 2: Truth maintenance system”. Preprints: 2nd IFSA Congress pp. 744–746.Google Scholar
Silverman, RH and Noetzel, A, 1990. “Image processing and pattern recognition in ultrasonograms by backpropagation”. Neural Networks 3 593–603.CrossRefGoogle Scholar
Sira-Ramirez, H, 1980. “Fuzzy state estimation in linear dynamic systems”. In: Proc. IEEE Conf. on Decision and Control 2 380–382.Google Scholar
Sira-Ramirez, H, 1979. “Evolution of fuzzy sets in linear dynamic systems”. In: Proc. 1979 lnt. Conf. on Cybernetics and Society Denver, CO.Google Scholar
Siy, P and Chen, CS, 1974. “Fuzzy logic for handwritten numerical character recognition”. IEEE Trans. Syst., Man Cybern. 4 570–575.Google Scholar
Sosnowski, ZA, 1990. “FLISP —A language for processing fuzzy data”. Funny Sets, and Systems 37 23–32.CrossRefGoogle Scholar
Sosnowski, ZA, 1991. “Data structures for representing and processing of fuzzy information in Lisp”. Computers and Artificial Intelligence 10(6) 561–571.Google Scholar
Stellakis, KM and Valavanis, KP, 1991. “Fuzzy logic-based formulation of the organizer of intelligent robotic systems”. Journal of Intelligent and Robotics Systems: Theory and Applications 4(1) 1–24.CrossRefGoogle Scholar
Stipanicev, D, De, Neyer M and Gorez, R, 1991. “Self-tuning self-organizing fuzzy robot control”. In: Robot Control (Troch, I, Desoyer, K and Kopacek, P, eds.). Pergamon Press, pp. 171–176.Google Scholar
Sugeno, M, 1985. IndustrialApplications of Fuzzy Control. North-Holland.Google Scholar
Sugeno, M and Nishida, M, 1985. “Fuzzy control of model car”. Fuzzy Sets and Systems 16 103–113.CrossRefGoogle Scholar
Sugeno, M and Murakami, K, 1985. “An experimental study on fuzzy parking control using a model car”. In: Industrial Applications of Fuzzy Control (Sugeno, M, ed). North-Holland, pp. 125–138.Google Scholar
Suh, Il, Hong, Jong Hyuck Hong, Sang-Rok, Oh and Kwang, Bae Kim, 1991. “Fuzzy rule-based position&force control of industrial manipulators”. In: Proc. IROS '91 International Workshop on Intelligent Robots and Systems, Osaka, Japan, pp. 111–116.Google Scholar
Takagi, T and Sugeno, M, 1985. “Fuzzy identification of systems and its applications to modelling and control”. IEEE Trans. Syst., Man Cybern. 15 116–132.Google Scholar
Takagi, H, 1990. “Fusion technology of fuzzy theory and neural network-survey and future directions”. In: Proc. 1990 mt. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 13–26.Google Scholar
Takagi, H and Hayashi, I, 1991. “Artificial neural networks driven fuzzy reasoning”. hit. J. Approximate Reasoning 5 191–212.CrossRefGoogle Scholar
Takagi, T and Sugeno, M, 1993. “Derivation of fuzzy control rules from human operator's control actions”. In: Proc. IFACSymp. on Fuzzy Information, Knowledge Representation and Decision Analysis Marseilles, France, pp. 55–60.Google Scholar
Takahashi, H and Minami, H, 1989. “Subjective evaluation modelling using fuzzy logic and a neural network”. Proc. 3rd mt. Congress on Fuzzy Systems Association Seattle, WA, pp. 520–523.Google Scholar
Takeuchi, T, Nagai, Y and Enomoto, N, 1988. “Fuzzy control of a mobile robot for obstacle avoidance”. Information Sciences 45(2) 231–248.CrossRefGoogle Scholar
Takeuchi, T, 1991. “An autonomous fuzzy mobile robot”. Advanced Robotics 5(2) 215–230.CrossRefGoogle Scholar
Tanaka, H, Uejima, S and Asai, K, 1982. “Linear regression analysis with fuzzy model”. IEEE Trans. Syst. Man ybern. (12)(6).Google Scholar
Tanaka, H and Watanabe, J, 1988. “Possibilistic linear systems and their applications to the linear regression model”. Fuzzy Sets and Systems 27 275–289.CrossRefGoogle Scholar
Tanaka, H and Ishibuchi, H, 1991. “Identification of possibilistic linear systems by quadratic membership functions of fuzzy parameters”. Fuzzy Sets and Systems 41 145–160.CrossRefGoogle Scholar
Tanscheit, R and Scharf, RJ, 1988. “Experiments with the use of a rule-based self-organizing controller for robotics applications”. Fuzzy Sets and Systems 26 195–214.CrossRefGoogle Scholar
Teichrow, J, Horskotte, E and Togai, M, 1989. “The fuzzy-C compiler: A software tool for producing portable fuzzy expert systems”. In: Proc. 3rd IFSA Congress, Int. Fuzzy Systems, Association, pp. 708–711.Google Scholar
Thomason, MG, 1977. “Convergence of powers of a fuzzy matrix”. J. Math Anal. Appl. 57 476–480.CrossRefGoogle Scholar
Tong, RM, 1976. “Analysis of fuzzy control algorithms using the relational matrix”. Int. J. Man-Machine Studies 8 679–686.CrossRefGoogle Scholar
Tong, RM, 1977a. “A control engineering review of fuzzy systems”. Automatica 13 559–569.CrossRefGoogle Scholar
Tong, RM, 1977b. “Analysis and control of fuzzy systems using finite discrete relations”. Int. J. Control 32.Google Scholar
Tong, RM, Beck, MB and Latten, A, 1980. “Fuzzy control of the activated sludge wastewater treatment process”. Automatica 16 695–701.CrossRefGoogle Scholar
Tsukamoto, Y, 1979. “Fuzzy logic based on Lukasiewicz logic and its application to diagnosis and control”. PhD Thesis, Tokyo Institute of Technology.Google Scholar
Tzafestas, SG, 1986. “Knowledge engineering approach to system modelling, diagnosis, supervision and control”. In: Proc. IFAC&IMACS Symp. on Simul. of Control Systems Vienna, Austria.Google Scholar
(Also, 1989, Systems Anal. Modelling Simul. 61–19.)Google Scholar
Tzafestas, SG, 1987. “Artificial intelligence techniques in control”. In: Al, Expert Systems and Languages in Modelling and Simulation (Kulikowski, C and Ferrate, G, eds.). North-Holland, pp. 55–67.Google Scholar
Tzafestas, SG (ed.), 1988. Knowledge-based System Diagnosis, Supervision and Control. Plenum.Google Scholar
Tzafestas, SG and Papanikolopoulos, N, 1988. “Intelligent PID control based on fuzzy logic”. In: Proc. IFAC Symp. on Distributed Intelligence Systems Methods and Applications (DIS '88) Varna, Bulgaria.Google Scholar
Tzafestas, SG, 1990a. “Petri-net and knowledge-based methodologies in Manufacturing systems modelling, simulation and control”. Systems & Control Encyclop.: Suppl. 1. Pergamon Press.Google Scholar
Tzafestas, SG, 1990b. “Al techniques in computer-aided manufacturing systems”. In: Knowledge Engineering (Adeli, H, ed). McGraw-Hill, pp. 212.Google Scholar
Tzafestas, SG and Papanikolopoulos, N, 1990. “Incremental fuzzy expert PID control”. IEEE Trans. Industrial Electron. 37(5) 365–371.CrossRefGoogle Scholar
Tzafestas, SG, 1991. “Adaptive, robust and fuzzy rule-based control of robotic manipulators”. In: Intelligent Robotic Systems (Tzafestas, SO, ed.). Marcel Dekker, pp. 313–419.Google Scholar
Tzafestas, SG, Stamou, GB and Watanabe, K, 1994. “Fuzzy reasoning through a general neural network model”. In: Fuzzy Reasoning in Information, Decision and Control Systems (Tzafestas, SG and Venetsanopoulos, AN, eds.). Kluwer, pp. 145–161.CrossRefGoogle Scholar
Tzafestas, SG, Hatzivasiliou, FV and Kaltsounis, SK, 1994. “Fuzzy logic design of a nondestructive robotic fruit collector”. In: Fuzzy Reasoning in Information, Decision and Control Systems (Tzafestas, SO and Venetsanopoulos, AN, eds.). Kluwer, pp. 553–561.CrossRefGoogle Scholar
Tzafestas, SG, Terzakis, S and Venetsanopoulos, AN, 1994. “Fuzzy parameter and state estimation”. In: Fuzzy Reasoning in Information, Decision and Control Systems (Tzafestas, SO and Venetsanopoulos, , eds.). Kluwer, pp. 347–368.CrossRefGoogle Scholar
Tzafestas, SG, Palios, L and Cholin, F, 1994. “Diagnostic expert system inference engine based on the certainty factors model”. Knowledge-Based Systems 7(1) 17–26.CrossRefGoogle Scholar
Tzafestas, SG and Venetsanopoulos, AN, 1994. Fuzzy Reasoning in Information Decision and Control Systems. Kluwer.Google Scholar
Tzafestas, SG and Stamou, G, 1994. “A fuzzy path planning algorithm for autonomous robots in an uncertain environment”. In: Proc. EURISCON '94: The 2nd European Robotics and Intelligent Systems Conference Malaga, Spain.Google Scholar
Uehara, K and Fujise, M, 1990. “Learning of fuzzy-inference criteria with artificial neural network”. In: Proc. Int. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 193–198.Google Scholar
Umano, M, Mizumoto, M and Tanaka, K, 1978. “FSTDS system: A fuzzy-set manipulation system”. Information Sciences 15 115–159.CrossRefGoogle Scholar
Umano, M, 1987a. “Fuzzy-Set Prolog”. Preprints: 2nd IFSA Congress, pp. 750–753.Google Scholar
Umano, M, 1987b. “Fuzzy-set manipulation system in Lisp”. Preprints: 2nd IFSA Congress, pp. 840–843.Google Scholar
Umbers, IG and King, PJ, 1980. “An analysis of human decision-making in cement kiln control and the implications for automation”, Int. J. Man-Machine Studies 12 11–23.CrossRefGoogle Scholar
Uragami, M, Mizumoto, M and Tanaka, K, 1976. “Fuzzy robot controls”. Journal of cybernetics 6(1–2) 39–64.CrossRefGoogle Scholar
Vachtsevanos, GJ, Davey, K and Lee, KM, 1987. “Development of a novel intelligent robotic manipulator”. IEEE Control Systems Magazine 7(3) 9–15.CrossRefGoogle Scholar
Valavanis, KP and Stellakis, KM, 1991. “A general organizer model for robotic assemblies and intelligent robotic systems”. IEEE Trans. on Systems, Man and Cybernetics 21(2) 302–317.Google Scholar
Van Nauta Lemke, HR and Dezhao, W, 1985. “Fuzzy PID supervisor”. In: Proc. 24th IEEE CDC, Paper WP 9–5: 15, pp. 602–608.Google Scholar
Van Amerongen, J, Van Nauta Lemke, HR and Van der Veen, CT. 1977. “An autopilot for ships designed with fuzzy sets”. In: Proc. 5th IFACIIFIP Int. Conf. on Digital Camp. Appl. to Process Control.CrossRefGoogle Scholar
Wakileh, BAN and Gille, KS, 1988. “Use of fuzzy logic in robotics”. Computers in Industry 10(1) 35–46.CrossRefGoogle Scholar
Watanabe, K, Jis, S and Tzafestas, SG, 1994. “Fuzzy control for robot manipulators with artificial rubber muscles”. In: Fuzzy Reasoning in Information, Decision and Control Systems. Kluwer, pp. 493–510.CrossRefGoogle Scholar
Wenstop, F, 1976. “Deductive verbal models of organizations”. Int. J. Man-Machine Studies 8 293–311.CrossRefGoogle Scholar
Witsenhausen, H, 1968. “Set of possible states for linear systems given perturbed observations”. IEEE Trans. Auto. Contr. 13(5) 556–558.CrossRefGoogle Scholar
Xu, Y, Paul, RP and Shun, HY, 1991. “Fuzzy control of robot and compliance wrist systems”. In: Proc. IEEE Industry Applications Society Annual Meeting pp. 1431–1437.Google Scholar
Yager, RR, 1992. “Implementing fuzzy logic controllers using a neural network framework”. Fuzzy Sets and Systems 48 53–64.CrossRefGoogle Scholar
Yamakawa, T, 1990. “Pattern recognition hardware system employing a fuzzy neuron”. In: Proc. Int. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 943–948.Google Scholar
Yamamoto, S, 1994. “Software representation of fuzzy sets and logic”. In: Fuzzy Reasoning in information, Decision and Control Systems (Tzafestas, SO and Venetsanopoulos, AN, eds.). Kluwer, pp. 52–68.Google Scholar
Yasunobu, S and Hasegawa, T, 1986. “Evaluation of an automatic container crane operation system based on predictive fuzzy control”. Control-Theory and Advanced Technology 2 419–432.Google Scholar
Yen, J, 1990. The Role of Fuzzy Logic in the Control of Neural Networks. In: Proc. Int. Conf. on Fuzzy Logic and Neural Networks. lizuka, Japan, pp. 771–774.Google Scholar
Zadeh, LA, 1965. “Fuzzy Sets”. Inform, and Control 8 338–353.Google Scholar
Zadeh, LA, 1969. “Fuzzy algorithms”. Inform, and Control 11 323–339.Google Scholar
Zadeh, LA, 1970. “Theory of approximate reasoning”. In: Machine Intelligence 9 (Hayes, JE, Michie, D and Mikulich, LI, eds.). Ellis Horwood, pp. 149–194.Google Scholar
Zadeh, LA, 1973. “Outline of a new approach to the analysis of complex systems and decision processes”. IEEE Trans. Syst, Man. Cybern, 3 28–44.Google Scholar
Zadeh, LA, 1975. “Fuzzy logic and approximate reasoning. (In Memory of Grigore Moisil)”. Synthèse 30 407–428.CrossRefGoogle Scholar
Zadeh, LA, 1978. “PRUF-A meaning representation language for natural languages”. Int. J. Man-Machine Studies 10 395–460.CrossRefGoogle Scholar
Zadeh, LA, 1983a. “A computational approach to fuzzy quantifiers in natural languages”. Comp. & Maths. 9 149–184.Google Scholar
Zadeh, LA, 1983b. “Commonsense knowledge representation based on fuzzy logic”. Computer 10, 63–65.Google Scholar
Zahzah, EH, Desachy, J and Zehana, M, 1992. “A fuzzy connectionist approach for a knowledge based image interpretation system”. In: Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 1135–1138.Google Scholar
Zhou, MC and Leu, MC, 1991. “Petri net modeling of a flexible assembly station for printed circuit boards”. In: Proc. 1991 IEEE mt. Conf. on Robotics and Automation Sacramento, pp. 2530–2535.Google Scholar
Zhuang, WP, Qiao, WZ and Heng, TH, 1990. “The truth-valued flow inference network”. In: Proc. 1990 Int Conf. on Fuzzy Logic and Neural Networks lizuka, Japan, pp. 267–281.Google Scholar
Zirnmermann, HJ, 1992. “Approximate reasoning in manufacturing”. In: Intelligent Design and Manufacturing (Kusiak, A, ed). Wiley, pp. 701–722.Google Scholar