Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    Belépés
    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Soft Computing Methods

    A tantárgy neve magyarul / Name of the subject in Hungarian: Lágy számítási módszerek

    Last updated: 2013. július 1.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    MSC programme (Informatics)

    Major: Autonomous control systems and robots

    Course ID Semester Assessment Credit Tantárgyfélév
    VIIIM129 1 2/1/0/v 4  
    3. Course coordinator and department Dr. Harmati István,
    4. Instructors

    Dr. István Harmati

    Dr. Bálint Kiss 

    5. Required knowledge Mathematics, Control Engineering
    6. Pre-requisites
    Kötelező:
    NEM ( TárgyEredmény( "BMEVIIIMA09" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIIIMA09", "FELVETEL", AktualisFelev()) > 0 )

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rendek grafikus formában itt láthatók.

    Ajánlott:
    -
    7. Objectives, learning outcomes and obtained knowledge

    The goal of the course is to introduce the state-of-the-art soft computing and artificial intelligence methods used in system modeling and control theory. The methods are introduced in the frame of nonlinear identification and control problems.

     

    Students successfully satisfying the course requirements are prepared in system modeling and to design and implement control algorithms for complex systems. In general, they are able to contribute to the solution system optimization and decision making problems. They obtain skills to apply fuzzy systems, neural networks, genetic algorithms and swarm intelligence on technological and nontechnological areas (e.g. biology, economics). Also, they are able to take part in the development and research of information system with high demand on artificial intelligence techniques.

    8. Synopsis

    14 weeks of classes: 26 hours of lectures + 13 hours of classroom practices. Classroom practices illustrate the methods with application examples. The topics of the lectures are the following:

     

    1. Fundamentals of fuzzy-neural systems. Fuzzy implication, defuzzification, Sugeno-type fuzzy systems.

     

    1. The block diagram of Fuzzy Logic Controllers (FLC), the functionality of the blocks, Fuzzy PID and PD controllers.  MacVicar-Whelan meta rules. The rule base design of Fuzzy PD controller. 

     

    1. Overview of numerical optimization methods. The necessary analytical condition of the optimal solution considering the constraints. The statement of optimization problem, aczive set, LICQ condition, the Lagrange function of the optimization problem. First order (Karush-Kuhn-Tucker) conditions.

     

    1. Optimization methods. Gradient-like, conjugate gradient, quasi Newton methods. Computation of gradient in neural networks. Subtractive clustering, computation of gradient in adaptive networks, ANFIS.

     

    1. The architecture of Genetic Algorithms. Linear and nonlinear fitness function, selection, binary and real genetic operators, reinsertation strategies. Multipopulation algorithm. Controller design with genetic algorithm.

     

    1. Adptive fuzzy control. Nominal and supervisory control. Indirect (model based) and direct adaptive control. Stability analysis.

     

    1. Direct adaptive neural control with full state feedback, adaptive control with neural network based nonlinear observer. Case study: flight control.

     

    1. Fuzzy approximation based on SVD. The algorithm, methods to satisfy the mathematical conditions, multivariable extension. Control  design with SVD technique.

     

    1. Optimization and control design with evolutionary programming and bacterial algorithms. The algorithms, guzzy interpretation, control design.

     

    1. Swarm intelligence. Motiovation, common properties, The definition of swarms and intelliogence. Ant Colony Optimization (ACO). The base of global behavior, the mathematical model of ants. The difference between the real and artificial ants. The role of pheromone. The methaheuristic of ACO.

     

    1. Particle swarm optimization (PS). Motiovations, benefits and drawbacks, the concept of PSO, the artificial swarm, optimization algorithm, the motion of particles. Implementation issues, discrete implementation, variants.

     

    1.  Learning algorithms. Algorithms that learn equilibria, best response. Bounfaries in computations, Wolf algorithm and its variants. The control of multiagent systems with learning algorithms.

     

     

    1. Probabilistic model with Bayes networks. 

     

     

    9. Method of instruction

    26 hours of lectures + 13 hours of classroom practices. 

     

     

     

     


    10. Assessment

    One midterm is written during the semester, its result must be at least 2 (on the scale of 1 to 5). The result of midterm gives 20 percent in the result of the finale exam. 

     

    11. Recaps

    The mid-term can be repeated once in the teaching period.


    12. Consultations

    One week before the midterm if required.


    13. References, textbooks and resources

    [1] Electronic slides on the educational portal: edu.iit.bme.hu (registration is necessary) 

    [2] B. Lantos: Fuzzy systems and genetic algorithms, 2002, Műegyetemi kiadó


    14. Required learning hours and assignment
    Kontakt óra42
    Félévközi készülés órákra15
    Felkészülés zárthelyire15
    Házi feladat elkészítése
    Kijelölt írásos tananyag elsajátítása
    Vizsgafelkészülés48
    Összesen120
    15. Syllabus prepared by

    Dr. Béla Lantos, Professor

    Dr. István Harmati, Associate Professor

    Dr. Gábor Vámos, Associate Professor