FUZZY LOGIC Reading List - CSCI 397 with Dr. J, California State University, Chico

CSCI 397:  FUZZY LOGIC





READING LIST

This reading list is provided to supplement the material presented in the textbook. Items in this list may also be used to supplement basic research in fuzzy logic and its applications in soft computing.


  • Fundamentals, Theoretical Foundations

    1. C. Elkan, Paradoxes of Fuzzy Logic, Revisited, tech. report, Dept. Computer Science and Engineering, UC San Diego, November 2000.
    2. B. Juliano, "An Introduction to Fuzzy Logic," guest lecture for CSCI 223 (Artificial Intelligence) at CSUC, March 1999.
    3. B. Juliano, "An Introduction to Fuzzy Logic," presented for CSU-Chico Mathematics Department's MATH-CSCI Colloquium, November 1998.
    4. B. Juliano, "What's so Fuzzy about Fuzzy Logic?," presented for the CSUC Math Club, October 1998.
    5. B. Juliano, "Fuzzy Logic and Control," presented for the CSUC chapter of the American Institute of Mechatronics Engineers (AIME), October 1998.
    6. S.D. Kaehler, "Fuzzy Logic Tutorial," Fuzzy Logic Tutorial - An Introduction, http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html [30 Oct. 2002].
    7. P. Klement and W. Slany, Fuzzy Logic in Artificial Intelligence, Tech. Report 94/67, Christian Doppler Laboratory for Expert Systems, Vienna, Austria, June 1997.
    8. F.M. McNeill and E. Thro, Fuzzy Logic: A Practical Approach, Academic Press, Inc., Chestnut Hill, MA, 1994.   [6.4 MB]
    9. J. Morris, "Author of FuzzyJess talks about AI and Java," August 31, 2004 http://www.devx.com/Java/Article/21848 [13 Sep. 2004].
    10. H.T. Nguyen and V. Kreinovich, ``Possible new directions in mathematical foundations of fuzzy technology: A contribution to the mathematics of fuzzy theory,'' Proc. Vietnam-Japan Bilateral Symposium on Fuzzy Systems and Applications (VJFUZZY `98), HaLong Bay, Vietnam, 9--32, Sep. 1998.
    11. R. Orchard, ``Fuzzy reasoning in Jess: The FuzzyJ Toolkit and FuzzyJess,'' Proc. Third Intl. Conf. Enterprise Info. Sys. (ICEIS 2001), Setubal, Portugal, pp. 533-542, July 2001.
    12. G. Prophet, "Fuzzy logic and neural nets: Still viable after all these years?," June 10, 2004 http://www.reed-electronics.com/ednmag/article/CA421505?text=fuzzy+logic [13 Sep. 2004].

  • Fuzzy Control - General

    1. J. Abonyi, L. Nagy, and F. Szeifert, ``Adaptive fuzzy inference system and its application in modelling and model based control'', Chemical Engineering Research and Design, vol. 77, no. 4, pp. 281-290, June 1999.
    2. S. Bentalba, A. El Hajjaji, A. Rachid, ``Fuzzy path tracking control of a vehicle,'' Proc. IEEE Intl. Conf. Intelligent Vehicles (IEEE IV`98), Stuttgart, Germany, pp. 195-200, Oct. 1998.
    3. H. Chekireb, M. Tadjine, and D. Bouchaffra, ``Direct adaptive fuzzy control of nonlinear system class with applications,'' Control and Intelligent Systems, vol. 31, no. 2, pp. 113-121.
    4. J. Godjevac, ``Comparative study of fuzzy control, neural network control and neuro-fuzzy control,'' in D. Ruan (Ed.), Fuzzy Set Theory and Advanced Mathematical Applications, Kluwer Academic, New York, Chapter 12, pp. 291-322, June 1995.
    5. F. Hoffmann, T. Koo, and O. Shakernia, ``Evolutionary design of a helicopter autopilot,'' In 3rd On-line World Conf. on Soft Computing (WSC3), 1998.
    6. A. Johansen, R. Shorten, and R. Murray-Smith, ``On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models,'' IEEE Trans. Fuzzy Systems, vol. 8, no. 3, pp. 297-313, June 2000.
    7. S. Kohn-Rich and H. Flashner, ``Robust fuzzy logic control of mechanical systems,'' Fuzzy Sets and Systems, vol. 133, no. 1, pp. 77-108, Jan. 2003.
    8. D. Nauck, F. Klawonn, and R. Kruse, ``Combining neural networks and fuzzy controllers,'' in E.P. Klement and W. Slany (Eds.), Fuzzy Logic in Artificial Intelligence, Springer-Verlag, Berlin, pp. 35-46, 1993.
    9. H.T. Nguyen, V. Kreinovich, and O. Sirisaengtaksin, ``Fuzzy control as a universal control tool,'' Fuzzy Sets and Systems, vol. 80, no. 1, pp. 71-86, June 1996.
    10. K.M. Passino and S. Yurkovich, Fuzzy Control, Addison Wesley Longman, Inc., Menlo Park, CA, 1998.   [5.6 MB]
    11. T.L. Seng, M. Bin Khalid, and R. Yusof, ``Tuning of a neuro-fuzzy controller by genetic algorithm,'' IEEE Trans. Sys., Man, and Cybernetics, Part B: Cybernetics, vol. 29, no. 2, pp. 226-236, Apr. 1999.
    12. D.E. Thomas and B. Armstrong-Helouvry, ``Fuzzy logic control - A taxonomy of demonstrated benefits'' Proc. IEEE, vol. 83, no. 3, pp. 407-421, Mar. 1995.
    13. S. Yurkovich and K.M. Passino, ``A laboratory course on fuzzy control,'' IEEE Trans. Education, vol. 42, no. 1, pp. 15-21, Feb. 1999.

  • Fuzzy Control and (Mobile) Robotics

    1. M.-R. Akbarzadeh et al., ``Soft computing for autonomous robotic systems,'' Intl. J. Computers and Electrical Engineering, vol. 26, no. 1, pp. 5-32, 2000.
    2. R. Alur, et al., ``A framework and architecture for multirobot coordination,'' Proc. Intl. Sym. on Experimental Robotics (ISER 2000), Honolulu, Hawaii, Dec. 2000.
    3. A. Chella, et al., ``Coordination of robot agency by fuzzy rules,'' Proc. Convegno Associazione Italiana per Intelligenze Artificiale (AIIA 2002), Sep. 2002.
    4. A.K. Das, et al., ``Real-time vision-based control of a nonholonomic mobile robot,'' Proc. IEEE Intl. Conf. Robotics and Automation (ICRA 2001), Seoul, Korea, May 2001.
    5. M.B.. Dias and A. Stentz, Enhanced Negotiation and Opportunistic Optimization for Market-Based Multirobot Coordination, Tech. Report CMU-RI-TR-02-18, The Robotics Institute, Carnegie Mellon University, Aug. 2002.
    6. M.B.. Dias and A. Stentz, A Market Approach to Multirobot Coordination, Tech. Report CMU-RI-TR-01-26, The Robotics Institute, Carnegie Mellon University, Aug. 2001.
    7. B.P. Gerkey and M.J. Mataric, ``Sold!: Market methods for multi-robot control,'' IEEE Trans. Robotics and Automation, vol. 18, no. 5, pp. 758-768, Oct. 2002.
    8. A. Homaifar, D. Battle, and E. Tunstel, ``Soft computing-based design and control for mobile robot path tracking,'' IEEE Intl. Symposium on Computational Intelligence in Robotics and Automation (CIRA 1999), Monterey, CA, pp. 35-40, Nov. 1999.
    9. T.L. Huntsberger et al., ``Rover autonomy for long range navigation and science data acquisition on planetary surfaces,'' Proc. 2002 IEEE International Conf. on Robotics and Automation (ICRA 2002), Washington, DC, pp. 3161-3168, May 2002.  Slides
    10. T.L. Huntsberger, P. Pirjanian, and P.S. Schenker, ``Robotic outposts as precursors to a manned Mars habitat,'' Proc. Space Technology and Appls. Intl. Forum (STAIF 2001), Albuquerque, NM, pp. 46-51, Feb. 2001.
    11. T.L. Huntsberger, ``Biologically inspired autonomous rover control,'' Autonomous Robots, vol. 11, no. 11, pp. 341-346, 2001.
    12. T.L. Huntsberger, G. Rodriguez, and P.S. Schenker, ``Robotics challenges for robotic and human Mars exploration,'' Proc. ROBOTICS 2000, Albuquerque, NM, pp. 84-90, March 2000.
    13. T.L. Huntsberger and J. Rose, ``Behavior-based control for autonomous mobile robots,'' Proc. ROBOTICS 2000, Albuquerque, NM, pp. 299-305, March 2000.
    14. M.J.. Mataric, Learning in Behavior-Based Multi-Robot Systems: Policies, Models, and Other Agents, 2001.
    15. M.N. Nicolescu and M.J. Mataric, ``Learning and interacting in human-robot domains,'' IEEE Trans. Sys., Man, and Cybernetics, Part A: Systems and Humans, vol. 31, no. 5, pp. 419-430, Sep. 2001.
    16. A. Ollero et al., ``Fuzzy tracking methods for mobile robots,'' in M. Jamshidi et al. (Eds.), Applications of Fuzzy Logic, Prentice-Hall, New Jersey.
    17. L.E.. Parker, ``Current state of the art in distributed autonomous mobile robotics,'' in L.E. Parker, G. Bekey, and J. Barhen, eds., Distributed Autonomous Robotic Systems 4, Springer-Verlag, Tokyo, pp. 3-12, 2000.
    18. G.A.S. Pereira, ``Distributed sensing for cooperative robotics.''
    19. G.A.S. Pereira, V. Kumar, and M.F.M. Campos, ``Decentralized algorithms for multirobot manipulation via caging,'' Proc. Intl. Workshop on Algorithmic Foundations of Robotics (WAFR 2002), Nice, France, Dec. 2002.
    20. B.S. Pimentel, G.A.S. Pereira, and M.F.M. Campos, ``On the development of cooperative behavior-based mobile manipulators,'' Proc. Intl. Joint Conf. Autonomous Agents and Multi-Agent Systems (AAMAS 2002), Bologna, Italy, pp. 234-239, July 2002.
    21. P. Pirjanian et al., ``CAMPOUT: A control architecture for multirobot planetary outposts,'' Proc. SPIE Conf. Sensor Fusion and Decentralized Control in Robotic Systems III, vol. 4196, Boston, MA, pp. 221-230, Nov. 2000.
    22. A. Saffiotti, ``Handling uncertainty in control of autonomous robots,'' Lecture Notes in Computer Science, vol. 1600, M.J. Wooldridge and M. Veloso, eds., pp. 381-408, 1999.
    23. A. Saffiotti, ``Fuzzy logic in autonomous robot navigation: a case study,'' Proc. 6th IEEE Intl. Conf. Fuzzy Systems, IEEE CS Press, pp. 573-578, 1997.
    24. A. Saffiotti, The uses of fuzzy logic in autonomous robot navigation: A catalogue raisonné, Tech. Report TR/IRIDIA/97-6, Université Libre de Bruxelles, Brussels, Belgium, 1997.
    25. A. Saffiotti, E.H. Ruspini, and K. Konolige, ``Using fuzzy logic for mobile robot control,'' Chapter 5 of Intl. Handbook Fuzzy Sets, D. Dubois, H. Prade, and H. Zimmermann, eds., Kluwer Academic Publishers Group, 1997.
    26. E. Tunstel et al., ``Fuzzy behavior-based navigation for planetary microrovers,'' Proc. of the NASA University Research Center Technical Conference, Berkeley, CA, pp. 355-359, June 1996.
    27. E. Tunstel et al., ``Soft computing paradigms for learning fuzzy controllers with applications to robotics,'' Proc. Biennial Conf. of the North American Fuzzy Info. Processing Society, Albuquerque, NM, pp. 729-734, Feb. 1997.
    28. E. Tunstel, T. Lippincott, and M. Jamshidi, ``Introduction to fuzzy logic with application to mobile robotics,'' Proc. First National Students Conf. of the National Alliance of NASA Univ. Research Centers, Greensboro, NC, March 1996.
    29. R. Zlot, et al., Market-Driven Multi-Robot Exploration, Tech. Report CMU-RI-TR-02-02, The Robotics Institute, Carnegie Mellon University, Jan. 2002.

  • Vision and Image Processing Systems

    1. S.A. Starks and V. Kreinovich, ``Aerospace applications of soft computing and interval computations (with an emphasis on multi-spectral satellite imaging),'' in M. Jamshidi, M. Fathi, and T. Furunashi (Eds.), Soft Computing, Multimedia, and Image Processing.
    2. H.R. Tizhoosh, "Fuzzy Image Processing," Homepage of Fuzzy Image Processing, http://watfor.uwaterloo.ca/tizhoosh/fip.htm [28 Oct. 2002].

  • Soft Computing

    1. A. Abraham and B. Nath, ``Hybrid intelligent systems design: A review of a decade of research,'' tech. report, School of Computing and Information Technology, Monash University, Australia, 2000.
    2. A. Abraham, ``Neuro-fuzzy systems: State-of-the-art modeling techniques,'' Lecture Notes in Computer Science, vol. 2084, pp. 269-???, 2001.
    3. H. Bersini and G. Bontempi, ``Now comes the time to defuzzify the neurofuzzy models,'' Fuzzy Sets and Systems, vol. 90, no. 2, pp. 161-170, 1997.
    4. U. Bodenhofer and F. Herrera, Ten Lectures on Genetic Fuzzy Systems, tech. report, Software Competence Center Hagenberg (SCCH), Austria, 1997.
    5. P.P. Bonissone et al., ``Hybrid soft computing systems: Industrial and commercial applications'' Proc. IEEE, vol. 87, no. 9, pp. 1641-1667, September 1999.
    6. P.P. Bonissone, ``Soft computing: The convergence of emerging reasoning technologies'' Soft Computing, vol. 1, no. 1, pp. 6-18, 1997.
    7. R. Fuller, Neural Fuzzy Systems, 1995.  Note: This is a large 253-page, 3.3MB document!
    8. H. Jacobsen, ``A generic architecture for hybrid intelligent systems,'' Proc. IEEE Intl. Conf. Fuzzy Systems (FUZZ-IEEE `98), 1998.
    9. J.R. Jang, ``ANFIS: Adaptive-network-based fuzzy inference systems,'' IEEE Trans. Sys., Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, May/June 1993.
    10. J.R. Jang and C. Sun, ``Neuro-fuzzy modeling and control,'' Proc. IEEE, vol. 83, no. 3, pp. 378-406, Mar. 1995.
    11. D. Nauck and R. Kruse, ``NEFCLASS: A neuro-fuzzy approach for the classification of data,'' in K.M. George et al. (Eds.), Proc. of the 1995 ACM Symposium on Applied Computing (Applied Computing 1995), Nashville, Tennessee, pp. 461-465, Feb. 1995.
    12. D. Nauck, ``Neuro-Fuzzy systems: Review and prospects,'' Proc. Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT `97), pp. 1044-1053, 1997.
    13. D. Nauck and R. Kruse, ``Neuro-fuzzy systems for function approximation,'' Fuzzy Sets and Systems, vol. 101, pp. 261-271, 1999.
    14. T. Ojala, Neuro-Fuzzy Systems in Control, M.S. thesis, Department of Electrical Engineering, Tampere University of Technology, Tampere, Finland, June, 1994.
    15. P.S.. Schenker et al., Planetary rover developments supporting mars exploration, sample return and future human-robotic colonization, Autonomous Robots, Vol. 14, pp. 103-126, 2003.
    16. S. Wermter and R. Sun, ``An overview of hybrid neural systems,'' Lecture Notes in Artificial Intelligence, LNCS 1778, S. Wermter and R. Sun, eds., pp. 1-13, 2000.

  • Natural Language Systems

    1. B.B. Rieger, ``Computational semiotics and fuzzy linguistics,'' 1997.
    2. B.B. Rieger, ``Semiotics and computational linguistics,'' 1999.

  • Classification

    1. J.T. Alander, An Indexed Bibliography of Learning Classifier Systems, Tech. Report 94-1-LCS, Department of Information Technology and Production Economics, University of Vaasa, Finland, Sep, 1999.
    2. M.V. Butz, XCSJava 1.0: An Implementation of the XCS classifier system in Java, Tech. Report 2000027, Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign, June, 2000.
    3. M.V. Butz and S.W. Wilson, ``An algorithmic description of XCS,'' Lecture Notes in Computer Science, vol. 1996, pp. 253-272, 2000.
    4. T. Kovacs, ``Learning classifier systems resources,'' Soft Computing, vol. 6, no. 3-4, pp. 240-243, 2002.
    5. T. Kovacs and P.L. Lanzi, A Learning Classifier Systems Bibliography, Tech. Report CSRP-99-19, School of Computer Science, University of Birmingham, Dec., 1999.
    6. T. Kovacs, ``XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions,'' in P. Chawdhry et al. (Eds.), Soft Computing in Engineering Design and Manufacturing, Springer-Verlag, London, pp. 59-68, 1997.
    7. S.W. Wilson, ``Mining oblique data with XCS,'' Lecture Notes in Computer Science, vol. 1996, pp. 158-???, 2001.
    8. S.W. Wilson, ``State of XCS classifier system research,'' Lecture Notes in Computer Science, vol. 1813, pp. 63-81, 1999.

  • Software Libraries/Tools

    1. National Aeronautics and Spaca Administration (NASA), ``CLIPS: A Tool for Building Expert Systems,'' http://www.ghg.net/clips/CLIPS.html [17 Dec. 2002].
    2. National Research Council Canada, ``NRC FuzzyJ ToolKit,'' http://ai.iit.nrc.ca/IR_public/fuzzy/fuzzyJToolkit.html [17 Dec. 2002].
    3. National Research Council Canada, ``FuzzyCLIPS,'' http://ai.iit.nrc.ca/IR_public/fuzzy/fuzzyClips/fuzzyCLIPSIndex.html [17 Dec. 2002].
    4. National Research Council Canada, ``FuzzyJess,'' http://ai.iit.nrc.ca/IR_public/fuzzy/fuzzyJavaDocs/FuzzyJess.html [17 Dec. 2002].
    5. Sandia National Laboratories (SNL) ``Jess, the Expert System Shell for the Java Platform,'' http://herzberg.ca.sandia.gov/jess/ [17 Dec. 2002].





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