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In order to avoid the premature convergence and improve convergence rate, a catastrophic adaptive genetic algorithm for reactive power optimization is discussed in detail. Before the germination of premature convergence, the cataclysm operator is adopted to update all individuals randomly except for the current optimum.When the change rate of average fitness is decreased to a critical condition; the...
In order to avoid the premature convergence and improve convergence rate, a novel adaptive genetic algorithm for reactive power optimization is discussed in detail. In reproduction operator, the method of retaining optimal individual is used to ensure the convergence and at the same time, the competition method is also adopted to keep the better dispersal of all individuals. In Mutation operator,...
This paper presents output feedback neural control for helicopters in single-channel modes of operation with dynamics in single-input single-output (SISO) nonlinear nonaffine form. A constructive approach for adaptive NN control design with guaranteed stability is proposed based on the use of the Implicit Function Theorem, Mean Value Theorem, and high gain observer. It is shown that the output tracking...
This paper presents a modification of the sentient particle swarm optimization algorithm intended to introduce some psychology factor of emotion into the algorithm. At the same time, the algorithm compares the real velocity and position error with the threshold for finding conditionality condition of dissipative particle. It guides individuals to behave reasonably with the capability of self-adaptation...
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