Mathematical Modeling using Fuzzy Logic

Neurofuzzy filters use system input and output data to learn system policies, so they do not use mathematical modeling. After learning the behavior of the system, the neurofuzzy filter automatically generates fuzzy rules and membership ...

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Author: Abhijit Pandit

Publisher: CRC Press

ISBN: 9780429751721

Category: Business & Economics

Page: 218

View: 166

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.

Fuzzy Logic

A formal mathematical model is indispensable when we wish to rigorously analyze these systems. In this chapter, we survey some recent results on transforming verbal descriptions into mathematical models using fuzzy modeling.

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Author: Paul P. Wang

Publisher: Springer

ISBN: 9783540712589

Category: Technology & Engineering

Page: 459

View: 852

How far can you take fuzzy logic, the brilliant conceptual framework made famous by George Klir? With this book, you can find out. The authors of this updated edition have extended Klir’s work by taking fuzzy logic into even more areas of application. It serves a number of functions, from an introductory text on the concept of fuzzy logic to a treatment of cutting-edge research problems suitable for a fully paid-up member of the fuzzy logic community.

Mathematical Modeling using Fuzzy Logic

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.

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Author: Abhijit Pandit

Publisher: CRC Press

ISBN: 9780429751714

Category: Business & Economics

Page: 218

View: 973

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.

Optimization Models Using Fuzzy Sets and Possibility Theory

Since the early success with fuzzy logic control and MYCIN, a very wide range of ESs have been developed. ... on the development of precise mathematical models and their use in the development of optimum control and decision algorithms.

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Author: J. Kacprzyk

Publisher: Springer Science & Business Media

ISBN: 9789400938694

Category: Mathematics

Page: 462

View: 736

Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be modelled or if state variables can only be described approximately. Until recently, everything which was not known with cer tainty, i. e. which was not known to be either true or false or which was not known to either happen with certainty or to be impossible to occur, was modelled by means of probabilitieso This holds in particular for uncertainties concerning the oc currence of events.

Handbook of Biosensors and Electronic Noses

This is not to say that fuzzy logic techniques should be regarded as a replacement for mathematical models . On the contrary , the latter will continue to be used and find further applications in process optimization through the ...

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Author: Erika Kress-Rogers

Publisher: CRC Press

ISBN: 0849389054

Category: Medical

Page: 722

View: 817

In developing the electronic nose and biosensor devices, researchers not only copy biochemical pathways, but also use nature's approach to signal interpretation as a blueprint for man-made sensing systems. Commercial biosensors have demonstrated their benefits and practical applications, providing high sensitivity and selectivity, combined with a significant reduction in sample preparation assay time and the use of expensive reagents. The Handbook of Biosensors and Electronic Noses discusses design and optimization for the multitude of practical uses of these devices including:

Mathematical Modeling of the Immune System in Homeostasis Infection and Disease

They studied the relationship of clonal dominance with memory cell attrition with an agent-based model. ... Enciso et al. demonstrated how discrete dynamic models can be transformed to continuous dynamic models using Fuzzy logic.

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Author: Gennady Bocharov

Publisher: Frontiers Media SA

ISBN: 9782889634613

Category:

Page:

View: 172

The immune system provides the host organism with defense mechanisms against invading pathogens and tumor development and it plays an active role in tissue and organ regeneration. Deviations from the normal physiological functioning of the immune system can lead to the development of diseases with various pathologies including autoimmune diseases and cancer. Modern research in immunology is characterized by an unprecedented level of detail that has progressed towards viewing the immune system as numerous components that function together as a whole network. Currently, we are facing significant difficulties in analyzing the data being generated from high-throughput technologies for understanding immune system dynamics and functions, a problem known as the ‘curse of dimensionality’. As the mainstream research in mathematical immunology is based on low-resolution models, a fundamental question is how complex the mathematical models should be? To respond to this challenging issue, we advocate a hypothesis-driven approach to formulate and apply available mathematical modelling technologies for understanding the complexity of the immune system. Moreover, pure empirical analyses of immune system behavior and the system’s response to external perturbations can only produce a static description of the individual components of the immune system and the interactions between them. Shifting our view of the immune system from a static schematic perception to a dynamic multi-level system is a daunting task. It requires the development of appropriate mathematical methodologies for the holistic and quantitative analysis of multi-level molecular and cellular networks. Their coordinated behavior is dynamically controlled via distributed feedback and feedforward mechanisms which altogether orchestrate immune system functions. The molecular regulatory loops inherent to the immune system that mediate cellular behaviors, e.g. exhaustion, suppression, activation and tuning, can be analyzed using mathematical categories such as multi-stability, switches, ultra-sensitivity, distributed system, graph dynamics, or hierarchical control. GB is supported by the Russian Science Foundation (grant 18-11-00171). AM is also supported by grants from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. SAF2016-75505-R, the “María de Maeztu” Programme for Units of Excellence in R&D (MDM-2014-0370) and the Russian Science Foundation (grant 18-11-00171).

Discovery

Mathematical Modeling Using Fuzzy Logic Interval Graphs . ” Discrete Mathematics Colloquium , University of Colorado , Denver CO , 18 October 1994 . JACOBSON , Jay A. , Captain and Assistant Professor .

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Author: United States Air Force Academy

Publisher:

ISBN: STANFORD:36105131827466

Category: Aeronautics, Military

Page:

View: 575

Optimization Based Model Using Fuzzy and Other Statistical Techniques Towards Environmental Sustainability

M.N. Abu Bakar, M. Othman, M.H. Din, N.A. Manaf, H. Jarimi, Design concept and mathematical model of a bi-fluid ... R.M. Aguilar, V. Muñoz, Y. Callero, Control application using fuzzy logic: design of a fuzzy temperature controller, ...

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Author: Samsul Ariffin Abdul Karim

Publisher: Springer Nature

ISBN: 9789811526558

Category: Technology & Engineering

Page: 81

View: 148

This book explores key examples concerning the implementation of information technology and mathematical modeling to solve issues concerning environmental sustainability. The examples include using fuzzy weighted multivariate regression to predict the water quality index at Perak River in Malaysia; using wireless sensor networks (WSNs) for a remote river water pollution monitoring system; deriving biomass activated carbon from oil palm shell; and assessing the performance of a PV/T air solar collector. The book offers a valuable resource for all graduate students and researchers who are working in this rapidly growing area.

The Industrial Electronics Handbook Five Volume Set

Namely, start with a verbal description of the biological system's behavior (e.g., foraging in ants) and, using fuzzy logic theory, obtain a mathematical model of this behavior that can be immediately implemented by artificial systems ...

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Author: Bogdan M. Wilamowski

Publisher: CRC Press

ISBN: 9780429584770

Category: Technology & Engineering

Page: 4052

View: 972

Industrial electronics systems govern so many different functions that vary in complexity-from the operation of relatively simple applications, such as electric motors, to that of more complicated machines and systems, including robots and entire fabrication processes. The Industrial Electronics Handbook, Second Edition combines traditional and new