A fuzzy-modeling method for the emulation of expert decision behavior or for static as well as dynamic systems is presented. The input – output dataset of the system – or expert behavior is changed using fuzzy-sets into examples in linguistic form. These resulting examples build the fundament of the machine learning process for rule production (ID3). The fuzzy sets are optimized in order to min...

| Controlling the movement of an autonomous mobile robot requires the ability to pursue strategic goals in a highly reactive way. We describe a fuzzy controller for such a mobile robot that can take abstract goals into consideration. Through the use of fuzzy logic, reactive behavior (e.g., avoiding obstacles on the way) and goal-oriented behavior (e.g., trying to reach a given location) are smo...

In this paper we present a robot vision mechanism for a multiagent robotic systems for controlling the behavior of the agents. For implementing this vision mechanism we consider the fuzzy technology which is very versatile in this situation. This mechanism offers the surveillance of the entire set of robots and contributes to their behavior. Index Terms — cooperative systems, intelligent contro...

There are various types of uncertainty in the real world. This is a motivation to investigate the behavior of uncertain phenomena. Random phenomena is one class of objective uncertain phenomena which has been well studied. Probability theory is an efficient tool to study the behavior of random phenomena. Besides randomness, fuzziness is a basic type of subjective uncertainty initiated by Zadeh....

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea leads to modeling of a plant with unknown structure by defining several types of dynamic behavior, ...

In this paper, we propose a novel approach to behavior-based control, which combines fuzzy logic with multiple objective decision theory. Fuzzy logic provides powerful tools for formulating sophisticated behavior-based systems for robot control. However, it suuers from problems associated with resolving be-havioral connicts, which are mainly due to the de-ciencies of fuzzy inferencing technique...

Intelligent robot navigation can be achieved using a control system comprised of a collection of special-purpose motion routines, or behaviors. An approach to behavior coordination in multi-behavior systems is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavi...

Neuro-fuzzy systems have been used for robot navigation applications because of their ability to exert human like expertise and to utilize acquired knowledge to develop autonomous navigation strategies. In this paper, neuro-fuzzy based system is proposed for reactive navigation of a mobile robot using behavior based control. The proposed algorithm uses discrete sampling based optimal training o...

We study the qualitative behavior of the positive solutions of a second-order rational fuzzy difference equationwith initial conditions being positive fuzzy numbers, and parameters are positive fuzzy numbers. More precisely, we investigate existence of positive solutions, boundedness and persistence, and stability analysis of a second-order fuzzy rational difference equation. Some numerical exa...