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How fuzzy inference system works? 

Fuzzy number
Fuzzy classification
Fuzzy set operations
Defuzzification
Adaptive neuro fuzzy inference system

Answers from top 9 papers

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Papers (9)Insight
Journal Article•DOI
An Efficient Configuration for Probabilistic Fuzzy Logic System
Geng Zhang, Han-Xiong Li 
01 Oct 2012-IEEE Transactions on Fuzzy Systems
31 Citations
The computational analyses of six different fuzzy systems demonstrate the inference efficiency of the proposed method.
Journal Article•DOI
Statistical analysis of the main parameters: in the fuzzy inference process
Ignacio Rojas, Julio Ortega, Francisco J. Pelayo, Alberto Prieto 
01 Mar 1999-Fuzzy Sets and Systems
28 Citations
The results obtained show that the defuzzifier and the T-norm operator are the most relevant factors in the fuzzy inference process.
Journal Article•DOI
Fuzzy relation equations and fuzzy inference systems: an inside approach
Giorgos Stamou, Spyros G. Tzafestas 
01 Dec 1999
34 Citations
Furthermore, giving a formal study of fuzzy partitions and some useful aspects of fuzzy associations and fuzzy systems, the paper can be used as a theoretical background for designing consistent fuzzy inference systems.
Open access•Journal Article•DOI
A fuzzy inference system applied to value of information assessment for oil and gas industry.
Martin J. Vilela, Gbenga Folorunso Oluyemi, Andrei Petrovski 
23 May 2019
23 Citations
To the authors’ knowledge, this is the first use of a fuzzy inference system in the domain of value of information.
Journal Article•DOI
Adaptive-tree-structure-based fuzzy inference system
Jianqin Mao, Jiangang Zhang, Yufang Yue, Haishan Ding 
01 Feb 2005-IEEE Transactions on Fuzzy Systems
40 Citations
In comparison with some existing fuzzy inference systems, it is shown that the FT is also of less computation and high accuracy.
Book Chapter•DOI
Multilayer Optimization Approach for Fuzzy Systems
Ivan Nunes da Silva, Rogério Andrade Flauzino 
01 Jan 2009
5 Citations
This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used.
Open access•Journal Article•DOI
A fuzzy inference model based on an uncertainty forward propagation approach
Luis M. de Campos, Antonio González 
01 Sep 1993-International Journal of Approximate Reasoning
10 Citations
The proposed model is able to handle very general kinds of facts and rules, and it also verifies the most usual properties required by a fuzzy inference model.
Journal Article•DOI
Fuzzy inference systems applied to the daily ultraviolet radiation evaluation (295–385 nm) from daily global radiation
Lourdes Ramirez Santigosa, Jesus Polo Martinez, Llanos Mora López, Mariano Sidrach de Cardona Ortín, Julian Blanco Gálvez 
01 Dec 2003-Solar Energy
9 Citations
The results obtained verify the good behavior of the fuzzy inference system calculated.
Journal Article•DOI
Optimization of type-1, interval type-2 and general type-2 fuzzy inference systems using a hierarchical genetic algorithm for modular granular neural networks
Patricia Melin, Daniela Sánchez 
01 Apr 2019
16 Citations
The results obtained show the effectiveness of the proposed method to design structures of fuzzy inference systems.

Related Questions

What is fuzzy set theory?3 answersFuzzy set theory is a mathematical framework that deals with uncertainty and vagueness. It provides a logical solution for introducing, evaluating, and comparing various scenarios related to sustainability. Fuzzy sets are different from crisp sets as they assign a truth value between 0 and 1, representing the degree of membership of an element in a set. Fuzzy set theory has been applied in various fields such as economics, finance, engineering science, and physics to solve problems involving systems of linear equations with uncertain parameters. It also plays a key role in handling uncertainty in mathematical problems through the use of intuitionistic fuzzy sets, which handle both membership and non-membership functions. Fuzzy set theory also incorporates notions of symmetry and similarity, which are useful for comparing and classifying objects in real-world applications.
What are the advantages and disadvantages of using fuzzy logic for this purpose?5 answersFuzzy logic has several advantages for various applications, including control systems, classification, clustering, and prediction. It can handle information that is uncertain, imprecise, and vague, making it suitable for dealing with real-life problems that contain a high degree of uncertainty. Fuzzy logic allows for the use of linguistic grades, which is useful in uncertainty management of linguistic evaluations. It also provides a way to approximate human decision-making in complex situations by considering factors such as situational context, emotions, and values. However, classical fuzzy logic has some disadvantages, such as the difficulty in forming, grounding, and using complex formulae for logical reasoning. Additionally, assigning optimal values for control parameters and designing optimal fuzzy rule tables can be challenging.
What is fuzzy expert systems?5 answersFuzzy expert systems are computer-based systems that use fuzzy logic to handle uncertainties generated by imprecise, incomplete, and/or vague information. These systems mimic the logical processes of human experts or organizations to provide advice in a specific domain of knowledge. They combine experimental and experiential knowledge with intuitive reasoning skills to aid decision-making. Fuzzy expert systems have been applied in various fields such as software fault diagnosis, medical diagnosis, and agriculture. They are used to manage uncertainty and solve problems that cannot be effectively addressed using conventional methods. Fuzzy expert systems utilize fuzzy inference and reasoning techniques to process incomplete and fuzzy information. These systems are designed to co-operate and coordinate in distributed environments. Overall, fuzzy expert systems provide a valuable tool for decision support and problem-solving under uncertainty.
What is fuzzy system in medical?5 answersA fuzzy system in medicine refers to the use of fuzzy logic, which is a soft-computing technique that considers truth and falsehood in a graded fashion. It is suitable for developing knowledge-based systems in medicine and can be applied in tasks such as disease diagnosis, treatment selection, and real-time patient monitoring. Fuzzy systems in medicine can be implemented through fuzzy expert systems, which are intelligent programs that use fuzzy inference and reasoning to handle incomplete and fuzzy information in medical diagnosis. These systems allow medical experts to define fuzzy variables and rules for any medical application, and users can input symptoms and obtain medical conditions through the inference engine. Fuzzy systems also play a role in processing and analyzing fuzzy data generated from medical examinations, aiding in clinical diagnosis, predicting patient outcomes, and optimizing decision support in medical diagnostics.
How modern irrigation system using fuzzy?5 answersModern irrigation systems utilize fuzzy control methodologies to improve efficiency and precision in water management. Fuzzy control systems combine traditional feedback control with fuzzy logic to make intelligent decisions based on input variables such as soil moisture levels. These systems can adjust the opening time of solenoid valves in real-time, ensuring precise irrigation and faster response times. Additionally, fuzzy computational algorithms are used to determine whether to open servo valves based on continuous monitoring of air temperature, humidity, and ground moisture. Intelligent fuzzy inference systems have also been developed to create prescriptive maps for controlling the rotation speed of central pivot irrigation systems, taking into account spatial variability in crops. These systems use remote sensing data and fuzzy logic to determine the optimal speed for water application in different zones. Overall, fuzzy control methodologies have proven to be effective in improving the accuracy and efficiency of modern irrigation systems.
What are the main steps in fuzzy inference system?9 answers

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