Some Reflections on Intelligent Control
Artificial Intelligence Review
A symbol-based intelligent control system with self-exploration process
Engineering Applications of Artificial Intelligence
Multi-sensor surveillance of indoor environments by an autonomous mobile robot
International Journal of Intelligent Systems Technologies and Applications
Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning
Artificial Intelligence Review
HyFIS-Yager-gDIC: a self-organizing hybrid neural fuzzy inference system realizing Yager inference
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Evolving robotic path with genetically optimised fuzzy planner
International Journal of Computational Vision and Robotics
Robotic path planning using hybrid genetic algorithm particle swarm optimisation
International Journal of Information and Communication Technology
Hi-index | 0.00 |
This paper proposes an intelligent control system called self-exploring-based intelligent control system (SEICS). The SEICS is comprised of three basic mechanisms, namely, controller, performance evaluator (PE), and adaptor. The controller is constructed by a fuzzy neural network (FNN) to carry out the control tasks. The PE is used to determine whether or not the controller's performance is satisfactory. The adaptor, comprised of two elements, action explorer (AE) and rule generator (RG), plays the main role in the system for generating new control behaviors in order to enhance the control performance. AE operates through a three-stage self-exploration process to explore new actions, which is realized by the multiobjective genetic algorithm (GA). The RG transforms control actions to fuzzy rules based on a numerical method. The application of the adaptor can make a control system more adaptive in various environments. A simulation of robotic path-planning is used to demonstrate the proposed model. The results show that the robot reaches the target point from the start point successfully in the lack-of-information and changeable environments.