Quantum Fuzzy Inference Based on Quantum Genetic Algorithm: Quantum Simulator in Intelligent Robotics
Successful sophisticated search solutions of intractable robotic task’s as global robust intelligent and cognitive smart control in unpredicted (unconventional)/hazard control situations or multi-criteria imperfect control goal is based on quantum control principles (as quantum neural network for deep machine learning or quantum genetic optimization algorithm). It is important in these cases to choose types and kind of quantum correlations, as example, between PID-controller in coefficient gain schedule. Extracted from classical states (as example, from modeling of control coefficient gain’s laws) quantum hidden correlations (that physically rigor and mathematically strong correctness, and corresponds to main qualitative properties in general of ill-defined control object) are considered as an additional physical computing and hidden quantum information resources. These information resources changes the time-dependent laws of the coefficient gains schedule of the traditional controllers as PID-controllers with guarantees the achievement of control goal in hazard situations. This article discusses the application of quantum genetic algorithm to automatically choice the optimal type and kind of correlations in the quantum fuzzy inference. Efficiency of quantum search algorithm in imperfect KB self-organization on the Benchmark system “cart – pole” demonstrated.
KeywordsQuantum computing simulator Quantum genetic algorithm Quantum fuzzy inference Intelligent robotic
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