Self-organized robust intelligent control
Quantum fuzzy inference in unpredicted/hazard environments and quantum soft computational intelligence toolkit in MatLab
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The information technology design of intelligent control systems and robust knowledge bases for unpredicted/hazard environments described. The information technology consists from two steps and bases on new types of computational intelligence toolkit as soft and quantum computing. The first step bases on Soft Computing Optimizer toolkit and fuzzy stochastic modeling methodology, and gives the possibility to design the robust knowledge bases. The processes of learning and adaptation of intelligent control system are realized, and as a result partial robustness of intelligent control system is a...
The information technology design of intelligent control systems and robust knowledge bases for unpredicted/hazard environments described. The information technology consists from two steps and bases on new types of computational intelligence toolkit as soft and quantum computing. The first step bases on Soft Computing Optimizer toolkit and fuzzy stochastic modeling methodology, and gives the possibility to design the robust knowledge bases. The processes of learning and adaptation of intelligent control system are realized, and as a result partial robustness of intelligent control system is achieved. The second step is based on Quantum Computing Optimizer toolkit and includes the model of quantum fuzzy inference. The applying of the quantum algorithms gives synergistic effect of additional computational information resource based on quantum hidden information extracting from the classical states of control laws. The implementation of quantum fuzzy inference in the structure of intelligent control system enables to achieve global robustness by applying a new design principle as quantum self-organization of knowledge bases.
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