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The automated evolutionary design of an optimal hierarchical fuzzy system combined with the use of Interval Type-2 Fuzzy Systems and the Beta basis function is considered in this study. The resulted proposed system is named the Hierarchical interval Type-2 Beta Fuzzy System (HT2BFS). For the learning process, two main optimizations steps are considered. The first one executes the structure learning...
In this paper we introduce the Interval type-2 Beta fuzzy set as a membership function in a Fuzzy Logic System (FLS). First order derivatives of type-1 and type-2 Beta functions were developed for designing fuzzy logic systems based on given input-output pairs. Then, the steepest descent algorithm is used to train Beta fuzzy basis functions to obtain the final fuzzy system. The performance of the...
In this paper, a Multi-Objective Extended Genetic Programming (MOEGP) algorithm is developed to evolve the structure of the Hierarchical Flexible Beta Fuzzy System (HFBFS). The proposed algorithm allows finding the best representation of the hierarchical fuzzy system while trying to attain the desired balance of accuracy/interpretability. Furthermore, the free parameters (Beta membership functions...
This paper provides an overview on a new evolutionary approach based on an intelligent multi-agent architecture to design Beta fuzzy systems (BFSs). The Methodology consists of two processes, a learning process using a clustering technique for the automated design of an initial Beta fuzzy system, and a multi-agent tuning process based on Particle Swarm Optimization algorithm to deal with the optimization...
In this paper the investigation is placed on the hierarchic neuro-fuzzy systems as a possible solution for biped control. An hierarchic controller for biped is presented, it includes several sub-controllers and the whole structure is generated using the adaptive Neuro-fuzzy method. The proposed hierarchic system focus on the key role that the centre of mass position plays in biped robotics, the system...
This paper proposes a new approach of automatic hierarchization for monolithic fuzzy systems based on an extension of the fuzzy Q-learning method. This approach contributes to the reduction of the fuzzy rules base without recourse to expert knowledge. It suggests firstly a new technique of automatic structural hierarchization, which advocates the association of the most correlated input variables'...
Numerous similarity measures between intuitionistic fuzzy sets are proposed in literature using different approaches. In this paper, relationship between some existing intuitionistic fuzzy similarity and distance measures are investigated. These relations are paramount for the choice of a similarity or a distance measure and its application for any research topic.
Since swarm intelligence allows self-organization into an unfamiliar environment and adapting behaviors through simple individuals' interactions, we propose to realize a swarm multirobot organization with a fuzzy control. We introduce in this paper a fuzzy system for avoiding the collaboration stagnation and to improve the counter-ant algorithm (CAA). The robots' collaborative behavior is based on...
The aim of this work is to present a particular design technique of hierarchical fuzzy controllers. The method makes an easy way to control complex systems with an automatic low complicated design. It was based on the systems capability to generate output informations when exited outside any control instance. This fact helps constructing input-output learning and testing data. This type of fuzzy controller...
This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot;...
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