László T. Kóczy

The paper gives an overview of various bacterial type evolutionary algorithms used for fuzzy rule based identification. In order to find an optimal rule base from the input-output training data set, several improved algorithms have been developed in recent years. The task is to increase the models’ accuracy and convergence speeds by modifying a part of the Mamdani-type inference system.

Telecommunication connections are highly reliable and manageable, however, the handling of several parts of the networks is problematic. One of these parts is the access network. The variegation of the applied technologies and the individual connections to the customers in access networks makes the preliminary estimation of the performance of the telecommunications services and troubleshooting difficult. There are existing methods which can handle such problems, but the telecommunications companies (TELCO) are continuously looking for newer and more efficient methods. In this paper some existing methods for performance evaluation and the prediction of the probable failures of the wire pairs of telecommunications access networks are reviewed and novel methods that are based on the measurements of the wire pairs and use computational intelligence, fuzzy inference methods and evolutionary models are introduced.

In this paper, the latest member of the FUzzy-BAsed character Recognizer (FUBAR) algorithm family with multi-stroke character support is presented. The paper summarizes the basic concept and development of multi-stroke FUBAR and compares the single-stroke, multi-stroke FUBAR algorithms with the most similar methods found in literature.

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.

This paper describes the problems relating to the complexity of modern waste management systems. We present a new approach to selecting a better waste management solution. For a large and complex system it is extremely difficult to describe the entire system by a precise mathematical model. Therefore, we propose the use of Fuzzy Cognitive Maps (FCM), its combination with the Bacterial Evolutionary Algorithm (BEA) and the system of systems approach to support the planning and decision making process of integrated systems.

Intelligent robot path planning require have very complex decision-making and computational processes. Collecting and calculating a high amount of data is one of the weakest points of a such system. In addition, it is necessarily processed in real-time on a limited computational capacity. In this paper, we propose some novel algorithms for coping with these problems and give some information about Fuzzy Situational Maps and their use as a multidimensional extension of Fuzzy Signatures. An example takes to the field of path planning map pre-optimization by Fuzzy Situational Map.