In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the ...

In this paper we present a new approach for reliable fall detection. The fuzzy system consists of two input Mamdani engines and a triggering alert Sugeno engine. The output of the first Mamdani engine is a fuzzy set, which assigns grades of membership to the possible values of dynamic transitions, whereas the output of the second one is another fuzzy set assigning membership grades to possible ...

Modeling of the plant growth can be visualized using the approach of Lindenmayer System (L-System). This L-System models the plants growth by following the production rules, which are the combination of grammar and mathematic formulae. In this paper, we propose the use of fuzzy mamdani, to model the plant growth based on the current environment condition. The varied amount of fertilizer, both o...

Mamdani systems can incorporate expert knowledge about an input-output relation in the form of IF-THEN rules expressed in natural language. This is a particularly attractive feature for modeling and simulation, but is also one that can be easily misused. Focusing on applied modeling and simulation, this paper discusses why Mamdani systems are not a useful tool to explore the logical consequence...

The original idea of reasoning and control within fuzzy rule bases was proposed by Zadeh [1], and was called the Compositional Rule of Inference (CRI) and had the disadvantage of running directly in the k-dimensional input space (where k is the number of variables) while being able however to describe multi-dimensional membership function distributions of arbitrary shape. Its modified version, ...

Genetic algorithms and evolution strategies are combined in order to build a multi-stage hybrid evolutionary algorithm for learning constrained approximate Mamdani-type knowledge bases from examples. The genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule-based systems. The propos...

This paper presents a new learning algorithm for the design of Mamdani-type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-...

Two fuzzy models for real estate appraisal, i.e. Mamdani-type and Takagi-Sugeno-Kang-type have been built with the aid of experts. Both models comprised 7 input variables referring to main attributes of a property being appraised. In order to determine the rule bases for both models an evolutionary algorithm has been applied. The experiments conducted using MATLAB software revealed that models ...