Progress in AI Research: Impact of Technology Strides

1. First of all, this lecture presents research experience such as immune system, genetic algorithm, particle swarm optimization, bacterial foraging, and its hybrid system and application to real system. This lecture will also show research experience and results of emotion for emotion robot by AI. From research experience, immune system, PSO (Particle Swarm Optimization), BF (Bacteria Foraging), and hybrid system can have strong optimization function for engineering fields. In detailed description, this lecture describes research background about immune network based intelligent algorithm, PSO based intelligent algorithm, bacteria foraging based intelligent algorithm, and the characteristic of novel algorithm fusioned by their algorithm. This one also illustrates motivation and background that these algorithms should be applied to in the industry’s automatic system. 2. Second, this lecture illustrates immune algorithm and applied to various plant to investigate the characteristics and possibility of application. As the detailed description, immune algorithm will described by studied material to investigate possibility of application to plant. It suggests condition for disturbance rejection control in AVR of thermal power plant and introduce first into tuning method of its controller. In the conventional genetic algorithm, it takes a long time to compute and could not include a variety of information of plant because of using sequential computing methods. That is some problem with making a artificial intelligence for optimization. In this lecture, by means of introducing clonal selection of immune algorithm into computing procedure, it will be showed advanced results. That is, it can be calculated simultaneously necessary information, transfer function, time constant, and etc., for plant operation condition. Therefore, computing time is about 30% shorter than that of the conventional genetic algorithm and 10.6% smaller in overshoot when it is applied to controller. 3. This lecture will introduce parameter estimation method by immune algorithm for obtaining model of induction motor. It will suggest immune algorithm based induction motor parameter estimation to obtain optimal value depending on load variation from these parameters. 4. Also, this lecture will introduce about intelligent system using GA-PSO. It will introduce Euclidean data distance to obtain fast global optimization not local optimization by means of using wide data and