What are the main steps in fuzzy inference system?
Answers from top 9 papers
More filters
| Papers (9) | Insight |
|---|---|
27 Jun 1994 11 Citations | Therefore, the authors propose an auto-tuning method of hierarchical fuzzy inference. |
| The procedure implements a set of logical rules that can be applied when calibrating the shapes of the membership functions of a fuzzy inference system. | |
23 May 2019 | To the authors’ knowledge, this is the first use of a fuzzy inference system in the domain of value of information. |
| The results obtained verify the good behavior of the fuzzy inference system calculated. | |
07 Jun 1992 24 Citations | It is demonstrated that fuzzy inference systems can be used effectively for function approximation. |
28 Citations | It incorporates an automated fuzzy inference mechanism and should find applications in many areas of knowledge engineering such as expert systems, linguistic controllers, intelligent data bases, modelling of complex systems and decision support systems. |
121 Citations | It incorporates an automated fuzzy inference mechanism and should find applications in many areas of knowledge engineering such as expert systems, linguistic controllers, etc. |
| The results obtained show the effectiveness of the proposed method to design structures of fuzzy inference systems. | |
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. |
Related Questions
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 are the different approaches to integrating fuzzy logic controllers with other techniques?3 answersDifferent approaches to integrating fuzzy logic controllers with other techniques include hybridization, comparative studies, implementation of different types of fuzzy logic controllers, and the use of optimization methods. Hybridization involves combining fuzzy logic controllers with other techniques to design stable adaptive controllers. Comparative studies compare the efficiency and performance of different types of fuzzy logic systems, such as type-2 fuzzy logic systems, interval type-2 fuzzy logic systems, and generalized type-2 fuzzy logic systems. Implementation of different types of fuzzy logic controllers, such as type-1 and interval type-2, is done to observe their behavior in controlling nonlinear systems. The use of optimization methods, such as genetic algorithms, particle swarm optimization, and ant colony optimization, helps in finding appropriate parameter values and structure of fuzzy systems.
What is Fuzzy SVM?4 answersFuzzy SVM is a classification model that combines fuzzy logic and support vector machines (SVM). It is used to handle uncertain and imbalanced data in machine learning tasks. Fuzzy SVM assigns fuzzy membership values to data samples, increasing the certainty of uncertain data. This is done in both the input space and feature space, with higher fuzzy membership assigned to minority samples compared to majority samples. By incorporating fuzzy logic, Fuzzy SVM improves the accuracy of traditional SVM models. It is particularly useful in applications such as cloth pattern recognition, where there are large intra-pattern variations. Fuzzy SVM has been shown to outperform traditional SVM-based methods in various experiments.
What is the Fuzzy Analytical Hierarchy Process and what is the steps and theory behind each step?3 answersThe Fuzzy Analytical Hierarchy Process (FAHP) is a method used for decision-making based on multiple criteria. It is an extension of the Analytical Hierarchy Process (AHP) that incorporates fuzzy logic to handle uncertainty and imprecision in decision-making. The steps involved in FAHP include: 1) determining the criteria to be considered, 2) pairwise comparison of the criteria to establish their relative importance, 3) assigning linguistic values or numerical values to represent the degree of preference or importance, 4) calculating the weights of the criteria using fuzzy arithmetic operations, and 5) aggregating the weights to make the final decision. The FAHP approach helps in reducing bias and uncertainty in decision-making by converting comparisons into numerical values and processing them using fuzzy logic.
How does fuzzy neural inference work?9 answers
How many layers are in neuro fuzzy inference?9 answers