One of the main robotics tasks is efficient autonomous mobile robots movement in previously unknown environment with obstacles and walls. Different strategies exist to design control systems to perform the robot movement. One of the simplest ways to control autonomous mobile robots is the usage of IF-THEN rules. The article shows that a rough frame of control system using IF-THEN rules can be d...

This paper presents a new approach to show the validity of the chaining syllogism for fuzzy IF-THEN rules and rule bases. Based on this approach new conditions are given on which the deduction scheme holds.

Developing reliable yet flexible software is a hard problem. Although modeling methods enjoy a lot of advantages, the exclusive use of just one of them, in many cases, may not guarantee the development of reliable and flexible software. Formal modeling methods ensure reliability because they use a rigorous approach to software development. However, lack of knowledge and high cost practically fo...

This paper proposes ajuzzy neural expert system (FNES) with the following two functions: (1) Generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; (2) Extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (I f -part) from a trained neural network. This paper also ...

This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-...

This paper presents clustering techniques (K-means, Fuzzy K-means, Subtractive) applied on specific databases (Flower Classification and Mackey-Glass time series) , to automatically process large volumes of raw data, to identify the most relevant and significative patterns in pattern recognition, to extract production rules using Mamdani and Takagi-SugenoKang fuzzy logic inference system types.

In this paper we propose a learning method of fuzzy if-then rules for pattern classification problems. We assume that each training pattern has a weight that describes its importance. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty of the fuzzy if-then rules are determined from the co...