Jin-Kul Lee

New approach to real-time adaptive learning control of neural networks based on an evolutionary algorithm (II)

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its superiority in the finding of the optimal solution in the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and a new method that guarantees the convergence of evolutionary mutations is proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied to each sampling time because the learning process of an estimation, selection, mutation is in real-time. These algorithms can be applied by people who do not have knowledge about the technical tuning of dynamic systems to design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against outside disturbances.

The Self-tuning PID Control Based on Real-time Adaptive Learning Evolutionary Algorithm

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

Adaptive Learning Control of Neural Network Using Real-Time Evolutionary Algorithm

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

The Real-Time Control Technique Over the Environment of Windows Using Virtual Machine Driver

We studied the technique which can control the real system without additional hardware drivers using virtual machine driver operated on the windows operating system. We showed the feasibility of the proposed scheme under the error and the delay of a sampling time on the multi task processing through the load test of the experiment using graphic user interface.

A NEW METHOD OF THE EVOLUTIONARY ALGORITHM FOR ADAPTIVE LEARNING CONTROL

In case of restricting the relation between the parents and the offspring as each one in order to learn in real time, a new numerical formula is proposed to solve the difficulty of adjusting the search region as reducing the individual by using the error generated from the sensor of the dynamic system. The mutation equation of evolutionary strategy uses the error that is generated from the dynamic system. Competitive individuals among total population are reduced with automatic adjustments of the search region in accord with the error. Therefore, it is possible to control the control object varied as time changes because control signal of the learning is generated in real-time. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection and mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.