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Fuzzy logic and NeuroFuzzy applications explained

TL;DR: This tutorial jumps right in to the power of Fuzzy without dragging you through the basic concepts of fuzzy logic.
Abstract: 1. Fuzzy Logic in Minutes. 2. Fuzzy Logic Primer. 3. Development Tools for Fuzzy Systems. 4. NeuroFuzzy Technologies. 5. Case Studies of Industrial Applications. 6. Fuzzy Design Cookbook. 7. Using the Software. 8. Comparing Fuzzy vs. Conventional Control. References. Index.
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Book ChapterDOI
01 Jan 2008
TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.
Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

846 citations

Book
01 Jan 2004
TL;DR: This chapter discusses how to construct a Fuzzy Expert System using the Dempster-Shafer Method, a simple, scalable, and scalable approach that automates the very labor-intensive and therefore time-heavy process of designing and implementing an Expert System.
Abstract: Preface. 1 Introduction. 1.1 Characteristics of Expert Systems. 1.2 Neural Nets. 1.3 Symbolic Reasoning. 1.4 Developing a Rule-Based Expert System. 1.5 Fuzzy Rule-Based Systems. 1.6 Problems in Learning How to Construct Fuzzy Expert Systems. 1.7 Tools for Learning How to Construct Fuzzy Expert Systems. 1.8 Auxiliary Reading. 1.9 Summary. 1.10 Questions. 2 Rule-Based Systems: Overview. 2.1 Expert Knowledge: Rules and Data. 2.2 Rule Antecedent and Consequent. 2.3 Data-Driven Systems. 2.4 Run and Command Modes. 2.5 Forward and Backward Chaining. 2.6 Program Modularization and Blackboard Systems. 2.7 Handling Uncertainties in an Expert System. 2.8 Summary. 2.9 Questions. 3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I. 3.1 Classical Logic. 3.2 Elementary Fuzzy Logic and Fuzzy Propositions. 3.3 Fuzzy Sets. 3.4 Fuzzy Relations. 3.5 Truth Value of Fuzzy Propositions. 3.6 Fuzzification and Defuzzification. 3.7 Questions. 4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II. 4.1 Introduction. 4.2 Algebra of Fuzzy Sets. 4.3 Approximate Reasoning. 4.4 Hedges. 4.5 Fuzzy Arithmetic. 4.6 Comparisons between Fuzzy Numbers. 4.7 Fuzzy Propositions. 4.8 Questions. 5 Combining Uncertainties. 5.1 Generalizing AND and OR Operators. 5.2 Combining Single Truth Values. 5.3 Combining Fuzzy Numbers and Membership Functions. 5.4 Bayesian Methods. 5.5 The Dempster-Shafer Method. 5.6 Summary. 5.7 Questions. 6 Inference in an Expert System I. 6.1 Overview. 6.2 Types of Fuzzy Inference. 6.3 Nature of Inference in a Fuzzy Expert System. 6.4 Modification and Assignment of Truth Values. 6.5 Approximate Reasoning. 6.6 Tests of Procedures to Obtain the Truth Value of a Consequent from the Truth Value of Its Antecedent. 6.7 Summary. 6.8 Questions. 7 Inference in a Fuzzy Expert System II: Modification of Data and Truth Values. 7.1 Modification of Existing Data by Rule Consequent Instructions. 7.2 Modification of Numeric Discrete Fuzzy Sets: Linguistic Variables and Linguistic Terms. 7.3 Selection of Reasoning Type and Grade-of-Membership Initialization. 7.4 Fuzzification and Defuzzification. 7.5 Non-numeric Discrete Fuzzy Sets. 7.6 Discrete Fuzzy Sets: Fuzziness, Ambiguity, and Contradiction. 7.7 Invalidation of Data: Non-monotonic Reasoning. 7.8 Modification of Values of Data. 7.9 Modeling the Entire Rule Space. 7.10 Reducing the Number of Classification Rules Required in the Conventional Intersection Rule Configuration. 7.11 Summary. 7.12 Questions. 8 Resolving Contradictions: Possibility and Necessity. 8.1 Definition of Possibility and Necessity. 8.2 Possibility and Necessity Suitable for MultiStep Rule-Based Fuzzy Reasoning. 8.3 Modification of Truth Values During a Fuzzy Reasoning Process. 8.4 Formulation of Rules for Possibility and Necessity. 8.5 Resolving Contradictions Using Possibility in a Necessity-Based System. 8.6 Summary. 8.7 Questions. 9 Expert System Shells and the Integrated Development Environment (IDE). 9.1 Overview. 9.2 Help Files. 9.3 Program Editing. 9.4 Running the Program. 9.5 Features of General-Purpose Fuzzy Expert Systems. 9.6 Program Debugging. 9.7 Summary. 9.8 Questions. 10 Simple Example Programs. 10.1 Simple FLOPS Programs. 10.2 Numbers.fps. 10.3 Sum.fps. 10.4 Sum.par. 10.5 Comparison of Serial and Parallel FLOPS. 10.6 Membership Functions, Fuzzification and Defuzzification. 10.7 Summary. 10.8 Questions. 11 Running and Debugging Fuzzy Expert Systems I: Parallel Programs. 11.1 Overview. 11.2 Debugging Tools. 11.3 Debugging Short Simple Programs. 11.4 Isolating the Bug: System Modularization. 11.5 The Debug Run. 11.6 Interrupting the Program for Debug Checks. 11.7 Locating Program Defects with Debug Commands. 11.8 Summary. 11.9 Questions. 12 Running and Debugging Expert Systems II: Sequential Rule-Firing. 12.1 Data Acquisition: From a User Versus Automatically Acquired. 12.2 Ways of Solving a Tree-Search Problem. 12.3 Expert Knowledge in Rules auto1.fps. 12.4 Expert Knowledge in a Database: auto2.fps. 12.5 Other Applications of Sequential Rule Firing. 12.5.1 Missionaries and Cannibals. 12.6 Rules that Make Themselves Refireable: Runaway Programs and Recursion. 12.7 Summary. 12.8 Questions. 13 Solving "What?" Problems when the Answer is Expressed in Words. 13.1 General Methods. 13.2 Iris.par: What Species Is It? 13.3 Echocardiogram Pattern Recognition. 13.4 Schizo.par. 13.5 Discussion. 13.6 Questions. 14 Programs that Can Learn from Experience. 14.1 General Methods. 14.2 Pavlov1.par: Learning by Adding Rules. 14.3 Pavlov2.par: Learning by Adding Facts to Long-Term Memory. 14.4 Defining New Data Elements and New: RULEGEN.FPS. 14.5 Most General Way of Creating New Rules and Data Descriptors. 14.6 Discussion. 14.7 Questions. 15 Running On-Line in Real-Time. 15.1 Overview of On-Line Real-Time Work. 15.2 Input/Output On-Line in Real-Time. 15.3 On-Line Real-Time Processing. 15.4 Types of Rules Useful in Real-Time On-Line Work. 15.5 Memory Management. 15.6 Development of On-Line Real-Time Programs. 15.7 Speeding Up a Program. 15.8 Debugging Real-Time Online Programs. 15.9 Discussion. 15.10 Questions. Appendix. Answers. References. Index.

439 citations

Journal ArticleDOI
12 Sep 2005
TL;DR: A heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control that is transparent to, and easily "tweaked" by, the prosthetist/clinician is presented.
Abstract: This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

423 citations


Cites background or methods from "Fuzzy logic and NeuroFuzzy applicat..."

  • ...MoM is the most widely used and preferred defuzzification method in applications of recognition and classification [25]....

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  • ...While not always required, this practice seems to be standard protocol in MBF construction [25], [26]....

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Journal ArticleDOI
TL;DR: An adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models and provides linguistic meaning to the connectionist architectures is proposed.

334 citations


Cites background from "Fuzzy logic and NeuroFuzzy applicat..."

  • ...They have several features which make them suitable for a wide range of knowledge engineering and scientific applications (see e.g. Constantin, 1995)....

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Patent
21 Aug 1996
TL;DR: An interactive intervention training system used for monitoring a patient suffering from neurological disorders of movement or a subject seeking to improve skill performance and assisting their training is described in this article, where an expert system and neural network determine a goal to be achieved during training.
Abstract: An interactive intervention training system used for monitoring a patient suffering from neurological disorders of movement or a subject seeking to improve skill performance and assisting their training. A patient (or trainee) station is used in interactive training. The patient (or trainee) station includes a computer. A supervisor station is used by, for example, a medical or other professional. The patient (or trainee) station and the supervisor station can communicate with each other, for example, over the Internet or over a LAN. The patient (or trainee) station may be located remotely or locally with respect to the supervisor station. Sensors collect physiologic information and physical information from the patient or subject while the patient or subject is undergoing training. This information is provided to the supervisor station. It may be summarized and displayed to the patient/subject and/or the supervisor. The patient/subject and the supervisor can communicate with each other, for example, via video, in real time. An expert system and neural network determine a goal to be achieved during training. There may be more than one patient (or trainee) station, thus allowing the supervisor to supervise a number of patients/subjects concurrently.

308 citations