Expert Systems and the Basics of Fuzzy Logic

Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.

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Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice

Advances in Environmental Engineering and Green Technologies - Fuzzy Expert Systems and Applications in Agricultural Diagnosis ◽

10.4018/978-1-5225-9175-7.ch009 ◽

2020 ◽

pp. 155-174

Author(s):

M. Kalpana ◽

A. V. Senthil Kumar

Keyword(s):

Fuzzy Logic ◽

Expert Systems ◽

Membership Function ◽

Disease Incidence ◽

Assessment Methodology ◽

Fuzzy Expert Systems ◽

Fuzzy Assessment ◽

Triangular Membership Function ◽

1000 Grain Weight ◽

Hill Number

Fuzzy expert systems are designed based on fuzzy logic and deal with fuzzy sets. Many fuzzy expert systems have been developed for diagnosis. Fuzzy expert systems are developed using fuzzification interface, enhanced fuzzy assessment methodology, and defuzzification interface. Fuzzification helps to convert crisp values into fuzzy values. By applying the enhanced fuzzy assessment methodology for rice, the yield parameters of rice can be diagnosed with number of tillers per hill, number of grains per panicle, and 1000 grain weight. Pest and disease incidence becomes simple for scientists. Enhanced fuzzy assessment methodology for rice uses triangular membership function with Mamdani's inference and K Ratio. Defuzzification interface is adopted to convert the fuzzy values into crisp values. Performance of the system can be evaluated using the accuracy level. Accuracy is the proportion of the total number of predictions that are correct. The proposed algorithm was implemented using MATLAB fuzzy logic tool box to construct fuzzy expert system for rice.

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Introduction to Expert Systems, Fuzzy Logic, Neural Networks, and Chaos Theory

Advances in Computational Intelligence and Robotics - Genetic Algorithms and Applications for Stock Trading Optimization ◽

10.4018/978-1-7998-4105-0.ch001 ◽

2021 ◽

pp. 1-10

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Expert Systems ◽

Chaos Theory ◽

Classifier Systems ◽

Biologically Inspired ◽

Trading System ◽

The World ◽

Computational Procedures ◽

Near Future

The power of genetic algorithms (GAs) and related expert systems such as fuzzy logic, neural networks, and chaos theory and other classifier systems is truly infinite in nature. The above stated procedures are sure to happen in the near future, and there is no chance for it not to occur. GAs, fuzzy logic, neural networks, and chaos theory are all biologically-inspired algorithmic procedures, as they all are linked to the world of biology in some way. Market represents the ideas of traders. In the present environment, the market is driven by the ideas generated by the use of these AI-based expert systems and it is causing huge competition in making profits. This chapter is planned to be a detailed introduction of various popular expert systems such as GAs, neural networks, fuzzy logic, and chaos theory and their usages. Researchers in the past have proved that these computational procedures could have far reaching effects in the stock trading system.

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Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision

Fuzzy Systems ◽

10.4018/978-1-5225-1908-9.ch041 ◽

2017 ◽

pp. 935-968

Author(s):

A. B. Bhattacharya ◽

Arkajit Bhattacharya

Keyword(s):

Prostate Cancer ◽

Fuzzy Logic ◽

Urinary Incontinence ◽

Expert Systems ◽

Domestic Animals ◽

Medical Diagnostics ◽

Classical Approach ◽

Medical Work ◽

Neurological Signs ◽

Fuzzy Expert Systems

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.

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The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677899 ◽

2019 ◽

Vol 28 (01) ◽

pp. 027-034 ◽

Cited By ~ 1

Author(s):

Laszlo Balkanyi ◽

Ronald Cornet

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Fuzzy Logic ◽

Knowledge Representation ◽

Expert Systems ◽

Frequency Analysis ◽

Medical Subject Headings ◽

Subject Headings

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

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A practical review and taxonomy of fuzzy expert systems: methods and applications

Benchmarking An International Journal ◽

10.1108/bij-04-2019-0178 ◽

2019 ◽

Vol 27 (1) ◽

pp. 81-136 ◽

Cited By ~ 1

Author(s):

Madjid Tavana ◽

Vahid Hajipour

Keyword(s):

Fuzzy Logic ◽

Conference Proceedings ◽

Expert Systems ◽

Future Research ◽

Specific Domain ◽

Comprehensive Review ◽

Content Type ◽

Fuzzy Expert Systems ◽

Journal Publications ◽

Inference Systems

Purpose Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems. Design/methodology/approach The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems. Findings The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field. Originality/value Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

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