FUZZY SUBOPTIMAL FEEDBACK CONTROLLER

The paper concerns the time-optimal control of objects described by a differential equation representing the second law of Newtonian mechanics and taking into account a discon- tinuous model of resistances to motion. Such a task has broad technical applications, especially in robotics. A fuzzy approach is used to design a suboptimal closed-loop con- trol structure, convenient in practice thanks to its many advantages, especially in respect to robustness

Intuitionistic Fuzzy Rule-Base Model for the Time Dependent Traveling Salesman Problem

The Traveling Salesman Problem is a well-known combinatorial optimization problem. There are many different extensions and modifications of the original problem, such as The Time Dependent Traveling Salesman Problem, this specific extension of the original Traveling Salesman Problem towards more realistic traffic conditions assessment. In the Time Dependent Traveling Salesman Problem the “distances” (costs) between nodes vary in time, they are considered longer during the rush hour period or in the traffic jam region, e.g. the city centre. In this article we introduce an even more realistic approach, the Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem. It is an extension of the Time Dependent Traveling Salesman Problem with the additional notion of intuitionistic fuzzy sets (which is a generalization of the original fuzzy sets). Our goal is to give a useful extended, alternative model instead of the original abstract problem. By demonstrating that the addition of intuitionistic fuzzy elements to quantify the intangible jam factors creates an inference system that approximates the tour cost in a more practical way. Hence, we are one step closer to offering a more realistic solution for the generalized Traveling Salesman Problem. The results of two simple toy examples showed the general effectiveness of the model

Identification of the initial rule-base of a multi-stroke fuzzy-based character recognition method with meta-heuristic techniques

This paper summarizes the basic concept of the designed a fuzzy-based character recognition algorithm family and the results of the optimization of its rule-base with two various meta-heuristic methods, the Imperialist Competitive Algorithm and the bacterial evolutionary algorithm. The results are presented and compared with two other methods from literature after a short overview of the recognition algorithm

Számítási intelligencia algoritmusok, rendszerek és modellek = Algorithms, systems and models in computational intelligence

Korábbi eredményeinkre építve javasoltuk egy evolúciós (pl. bakteriális, részecskeraj) memetikus algoritmuscsaládot, az LM, maxi gradiens, és kombinációs eljárásokat alkalmaztunk lokális keresésre. Az új módszerek jobb konvergenciasebességgel és –pontossággal rendelkeznek, különösen a fuzzy modellek konstrukciójában. Javaslatot tettünk multipopulációs, többszálas és hibrid evolúciós, iteratív mohó és ütemezett vegyes evolúciós és memetikus eljárásokra. Szabványos adathalmazokon e módszerekkel az eddig publikált eredményeknél jobbat értünk el. Vizsgáltuk a fuzzy neurális hálózatokat, új struktúrákat, műveleteket bevezetve megkezdtük a hardver implementációt; fuzzy kognitív térképeket vizsgáltunk. Javaslatot tettünk a fuzzy szignatúrák geometriailag struktúrált általánosítására, változó finomságú szituációs térképek leírására. Javasoltuk a fuzzy 2 dimenziós raszterek alkalmazását a képreprezentációban. Az új komplex fuzzy - evolúciós/mohó/gradiens alapú optimalizációs - neurális hálózat eszközkészletet a műszaki és alkalmazott problémák széles körében használtuk fel, így a távközlési, a szállítási és logisztikai hálózatok optimalizációjára, hibadetektálásra; intelligens mobil robotok irányítására, kommunikációjára és autonóm együttműködésére; ellátási láncok és gyártási folyamatok optimalizálására; erőforrásallokációra és –ütemezésre; karakterfelismerésre és az építő- és környezetmérnöki döntéstámogatásra. | Based on our earlier research results we proposed a family of enhanced bacterial and evolutionary other memetic algorithms (e.g. Partical Swarm Optimization), with Levenberg-Marquard and Steepest Descent, viz. combinatorial methods for local search. The new methods have better convergence speed and accuracy, especially in fuzzy rule based model construction. We proposed multipopulation, multithread and hybrid evolutionary, iterative greedy and alternatingly scheduled mixed evolutionary and memetic approaches. We have achieved better results for standard benchmark data sets than any other authors. We studied neural networks based on fuzzy operations, proposing new structures, new operation families and starting hardware implementation; and we simulated fuzzy cognitive maps. We proposed extended classes of fuzzy signatures with geometric structure, modeling situational maps with flexible depth and fineness. We proposed fuzzy 2D grids for image representation. The new complex fuzzy - evolutionary/greedy/gradient optimization - neural network tool kit thus developed was deployed for a wide variety of engineering and applied problems, telecommunication, transport and logistic network optimization and failure detection, intelligent and mobile robot control, communication and co-ordination of autonomous collaboration; optimization of supply chains and production, resource allocation and scheduling, character recognition, and decision support in civil and environmental engineering

Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures

In historical district at European cities it is a major problem how to take decision on renovating or replacing existing buildings. This problem is imminent in Budapest (Hungary) in many traditional districts such as the Ferencváros district where we selected a compound area for further examination. By financial aid for the renovation of these buildings which awarded by Municipal Assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. In this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of L-fuzzy object). Using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. Linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. Such linguistic calculations might be of help for the Municipal Assembly awarding financial support. A complete example wit 26 buildings is presented