Integrated System for Intelligent Control
Integrated System for Intelligent Control
Intelligent Control Systems Using Soft Computing Methodologies
Intelligent Control Systems Using Soft Computing Methodologies
Artificial Intelligence: Where Has it Been, and Where is it Going?
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Robotics: Modelling, Planning and Control
Robotics: Modelling, Planning and Control
Grammatical inference methodology for control systems
WSEAS Transactions on Computers
Language identification of controlled systems: modeling, control, and anomaly detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In Part 1 of this article, a generic architecture was reported in conjunction with knowledge base learning control system (KBLCS). When implemented, the architecture is mapped onto a specialized software that uses artificial intelligence (AI) methods such as expert system in control problem solving. The intelligent controller represents the driving force that allows intelligent machines to achieve prescribed goals autonomously and embodies a symbolic capability for generating knowledge via inferences as well as a crude data management system using a numeric functionality for conventional control. The controller is composed of a rule base, a fact base and an inference engine. When environments treat more than one state of the process to be controlled, then it is careful to separate between control and inference, both functionally and architecturally. As the core of the generic architecture described earlier, we now report the main components of an intelligent controller. Robot control and grammatical control are taken as special applications of the proposed intelligent controller.