63,822 research outputs found

    Fuzzy rendszerek és modellek elemzése és identifikációja = Analysis and identification of fuzzy systems and models

    Get PDF
    Új algoritmuscsaládot fejlesztettünk ki, a bakteriális memetikus algoritmusokat, a bakteriális evolúciós algoritmust globális, a Levenberg-Marquard algoritmust pedig lokális keresésként alkalmazva. Ez az eljárás jobb algoritmusokat eredményezett az ismert hasonló módszereknél a pontosság és a ciklusszám összefüggésében; ezt különböző referencia alkalmazások és más példák segítségével bizonyítottuk. Az alkalmazások másik csoportját a logisztika adta. Kiterjesztettük Kano minőségi modelljét fuzzy exponensekre, melyet BMA-val optimalizáltunk és megkezdtük az utazó ügynök probléma közelítő megoldásának vizsgálatát is. Megmutattuk, hogy a fuzzy szabályinterpoláció számos valós probléma megoldására alkalmas. Sikeresen foglalkoztunk komplex forgalomirányítási alkalmazásokkal, továbbá vasúti menetrend és késés miatti átütemezés kérdéskörével. Szoftverrendszert implementáltunk, mely nagyszámú fuzzy következtetési és irányítási algoritmus összehasonlítására alkalmas. Kiterjesztettük a fuzzy szignatúrákat hierarchikus struktúrákra is és a Mamdani algoritmusra is. A fuzzy szignatúrákat robotok mozgásirányítására és kommunikációjára alkalmaztuk. E robotokat szimulációs technikával és saját fejlesztésű hardver segítségével is vizsgáltuk. Új kutatási részterületet indítottunk el a fuzzy műveletek és a rajtuk alapuló fuzzy flip-floppok vizsgálatával, melyekből konnekcionista rendszereket hoztunk létre és e fuzzy neurális hálózatokat modellkonstrukcióra és approximációra alkalmaztuk. | We developed a new family of algorithms, the Bacterial Memetic Algorithms by combining the Bacterial Evolutionary Algorithm as a global search and the Levenberg-Marquard algorithm as a local search method. This approach provided better algorithms in terms of approximation accuracy and population cycles than other similar approaches in the literature, as it was evidenced by various benchmark and real life applications. Another group of successful applications is in the logistics area. We extended Kano’s quality model to fuzzy exponents, optimized by BMA, and we started to research for the approximate solution of the Traveling Salesman Problem.We showed that fuzzy rule interpolation could be deployed for a number of real application areas. We dealt with complex traffic control applications as well as with railway time table and delay triggered rescheduling problems successfully. We implemented a software for the comparison of a large number of fuzzy reasoning and control algorithms. We extended Fuzzy Signatures to both hierarchical structures and Mamdani’s algorithm. We applied Fuzzy Signatures for motion control and fuzzy communication of robots. Such robots were investigated both in simulation and hardware construction developed by ourselves. We started a new research sub-direction by analyzing fuzzy operators and fuzzy flip-flops based on them. We built connectionist systems from them and we used these fuzzy neural networks for model construction and approximation

    Developing a Model of an Intelligent Control Technique on a Manufacturing Batching Process

    Get PDF
    Complex control algorithms are applied to manufacturing systems for certain process requirements, according to product specifications. When implementing specific complex control algorithms, primary and secondary conditions affect each other, affecting the measuring and control processes. While complex control algorithms result in several benefits, problems associated with mathematical reasoning and time delays need to be considered for an intelligent decision-making control technique to optimise control of the manufacturing process. The research will derive a suitable control technique by means of an adaptive neuro-fuzzy inference system, to optimise the manufacturing process. The paper will discuss technical aspects, the experimental set-up and the design process. Completed research on industrial Siemens FuzzyControl++ design tool and current research on MatLab Fuzzy Logic Toolbox will form part of the discussion on the design process. The paper will conclude with a comparison of various analysis results in MatLab Fuzzy Logic Toolbo

    AI and OR in management of operations: history and trends

    Get PDF
    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Comparison of different strategies of utilizing fuzzy clustering in structure identification

    Get PDF
    Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches
    corecore