Mathematical Modeling using Fuzzy Logic has been a dream project for the author. Fuzzy logic provides a unique method of approximate reasoning in an imperfect world. This text is a bridge to the principles of fuzzy logic through an application-focused approach to selected topics in engineering and management. The many examples point to the richer solutions obtained through fuzzy logic and to the possibilities of much wider applications. There are relatively very few texts available at present in fuzzy logic applications. The style and content of this text is complementary to those already available. New areas of application, like application of fuzzy logic in modeling of sustainability, are presented in a graded approach in which the underlying concepts are first described. The text is broadly divided into two parts: the first treats processes, materials, and system applications related to fuzzy logic, and the second delves into the modeling of sustainability with the help of fuzzy logic.
This book offers comprehensive coverage of the most essential topics, including:
Treating processes, materials, system applications related to fuzzy logic
Highlighting new areas of application of fuzzy logic
Identifying possibilities of much wider applications of fuzzy logic
Modeling of sustainability with the help of fuzzy logic
The level enables a selection of the text to be made for the substance of undergraduate-, graduate-, and postgraduate-level courses. There is also sufficient volume and quality for the basis of a postgraduate course. A more restricted and judicious selection can provide the material for a professional short course and various university-level courses.
Table of Contents
Preface. Acknowledgments. Author. 1. Introduction. 2. Sources of Uncertainty. 3. Membership Functions and Uncertainty. 4. Case Studies. 5. Singleton Type 1 Fuzzy Logic Systems: No Uncertainties. 6. Centroid of a Type 2 Fuzzy Set: Type Reduction. 7. Modeling of Sustainability. 8. Epilogue. Appendix A: Join, Meet, and Negation Operations for Non-Interval Type 2 Fuzzy Sets. Appendix B: Properties of Type 1 and Type 2 Fuzzy Sets. Appendix C: Computation. Appendix D: Medical Diagnosis by Fuzzy Logic. Appendix E: Fuzzy Logic System Optimized. Appendix F: Heart Disease Demo. Appendix G: Linear Tip Demo, Mamdani Tip Demo, Sugeno Tip Demo. Appendix H: Miscellaneous. Index.
Dr Abhijit Pandit is presently working as Assistant Professor at Amity University, Kolkata. He has more than 14 years of full-time teaching experience in reputed institutions. He is a Ph.D.(Univ. of Calcutta), M.B.A.(MAKAUT), M.Sc.(Univ. of Calcutta), B.Sc.(H) (Univ. of Calcutta) .Moreover, he is a MIMA(Member of Indian Management Association),Lifetime Member of Operational Research Society of India(ORSI), International Association of Engineers(IAENG) Society of Operations Research, Calcutta Mathematical Society. Till date author has published 18 research papers in reputed journals, 4 full-books (International Publishers), 2 book chapters (International Publishers). He has presented research papers in 23 conferences and also become keynote speaker in 2 such conferences. Author’s areas of interest are Quantitative Techniques, Fuzzy Mathematics, Marketing, Consumer Psychology, Operations Research, Service Marketing, and Healthcare Research. Apart from teaching and research, author is interested in music, social-service, sports etc.