This paper proposes a neural network for building and optimizing fuzzy models. The network can be regarded both as an adaptive fuzzy inference system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phas...

Nowadays, people start to accept fuzzy rule–based systems as flexible and convenient tools to solve a myriad of ill–defined but otherwise (for humans) straightforward tasks such as controlling fluid levels in a reactor, automatical lens focussing in cameras and adjusting an aircraft’s navigation to the change of winds and so on. Contrary to the intuition often seen as the feeding ground of fuzz...

This article presents a study on the use of parametrized operators in the Inference System of linguistic fuzzy systems adapted by evolutionary algorithms, for achieving better cooperation among fuzzy rules. This approach produces a kind of rule cooperation by means of the inference system, increasing the accuracy of the fuzzy system without losing its interpretability. We study the different al...

This paper proposes a Fuzzy Metagraph based Knowledge representation of Decision Support System (DSS). This system will help users to make correct decision with very low risk. Fuzzy Metagraph is an emerging technique widely used for real world applications. Fuzzy Metagraph is used to form the rule base to support inference system to make correct decision. This method can be used in many real wo...

Date grading is an important process for producers and affects the fruit quality evaluation and export market. However, the high costs, low speed and variation associated with manual sorting have been forcing the post harvest industry to apply mechanization and automation in sorting operations. As a step toward mechanized grading, in this research Mamdani fuzzy inference system was applied as a...

In this paper, a Fuzzy Inference System (FIS) is fabricated on a riderless bicycle. The Fuzzy Inference System is based on a rule base inherited from human experience of bicycle riding. The steady turning motion and roll-angle tracking controls for the riderless bicycle were achieved by using fuzzy concept. Collection of sensors, actuator, micro-controller and electrical circuits were employed ...

In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the functional representation of rules (fuzzy implications). In this paper, we extend methodology for selecting relevant fuzzy implications from [14] in backward reasoning. The proposed methodology takes full advantage of the functional representation of fuzzy implications and the algebraic properties ...

System modelling based on conventional mathematical tools like differential equations is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system, employing fuzzy if–then rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. This fuzzy modeling or fuzzy identification e...