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What is Fuzzy Inference System (FIS)

Chapter 108
Advanced Methodologies and Technologies in Business Operations and Management
The system which is a way of mapping an input space to an output space by using fuzzy set theory. FIS uses a collection of fuzzy membership functions and rules to generate an output.
Published in Chapter:
Fuzzy Logic Approach in Risk Assessment
Çetin Karahan (Directorate General of Civil Aviation, Turkey), Esra Ayça Güzeldereli (Afyon Kocatepe University, Turkey), and Aslıhan Tüfekci (Gazi University, Turkey)
Copyright: © 2019 |Pages: 20
DOI: 10.4018/978-1-5225-7362-3.ch108
Abstract
Risk is the likelihood of occurrence of any event that may obstruct the ability of organizations to achieve their strategic, financial, and operational goals. It is of profound importance for the business management to detect risks and determine appropriate actions in time. Risk assessment is a continuous and recursive process aimed at maximization of the use of opportunities while minimizing threats. There is a tendency in the field of risk assessment to prefer more quantitative methods to reduce unclarity. One such method is fuzzy logic. This chapter investigates fuzzy logic as an alternative to the classical methods that have been used for the purposes of risk assessment, which plays a crucial role in business action plans. Due to its similarity to the process of human reasoning and its success in cases of unclarity, fuzzy logic offers a number of advantages in this regard.
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Chapter 588
Fuzzy Logic Approach in Risk Assessment
The system which is a way of mapping an input space to an output space by using fuzzy set theory. FIS uses a collection of fuzzy membership functions and rules to generate an output.
Published in Chapter: Fuzzy Logic Approach in Risk Assessment; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 8
Adaptive Neuro-Fuzzy Inference System in Agriculture
The FIS is used for mapping the inputs to outputs by utilizing the fuzzy set theory. The inputs and outputs represent features and classes respectively with regards to fuzzy classification.
Published in Chapter: Adaptive Neuro-Fuzzy Inference System in Agriculture; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 16
Biometric Identification System Using Neuro and Fuzzy Computational Approaches
A FIS is a way of mapping an input space to an output space using fuzzy logic. FIS uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data.
Published in Chapter: Biometric Identification System Using Neuro and Fuzzy Computational Approaches; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 15
Automatic Detection of Tumor and Bleed in Magnetic Resonance Brain Images
Fuzzy inference systems have been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. Because of its multidisciplinary nature, the fuzzy inference system is known by a number of names, such as fuzzy rule-based system.
Published in Chapter: Automatic Detection of Tumor and Bleed in Magnetic Resonance Brain Images; From: Handbook of Research on Information Security in Biomedical Signal Processing
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