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This paper presents an Elman neural network based on Genetic algorithms for the identification of dynamic equivalents of power system. The Elman neural network is one of the dynamic recurrent neural networks. In this paper, a modified Elman network is introduced first. Then we propose its training algorithm using Genetic algorithms. Lastly, the proposed method is demonstrated and compared with the...
In this paper, we propose a self adaptive bacterial foraging optimization (SA-BFO) approach to update the proportional, integral, and derivative (PID) parameters which are optimized for estimating the fuzzy PID controller performance. In order to reduce or remove the steady state error of conventional controller, so we employ the fuzzy interference structure to adjust the PID controller gains and...
This paper presents a searching method for parameters identification of three phase synchronous generator by using a real-parameter genetic algorithm (GA). It is well known that GA method is an optimal or near optimal search technique borrowing the concepts from biological evolutionary theory. The ordinary form of GA used for solving a given optimization problem is a binary encoding during operating...
Belief measures are widely applied to management of uncertainty in information fusion. In most published applications, the estimations of belief measures that come from empirical rescouses, such as expert systems, are considered to be real belief measures without any validation. We proposed an efficient algorithm that can quickly detect the contradiction between the estimation and requirements of...
Aiming at Automated Guided Vehicle (AGV) dynamic model characteristics, a Variable Structure Control based on genetic algorithm (GA) and least square-support vector machine (LS-SVM) was designed. Parameters, predetermined by conventional reaching law, were regulated by LS-SVM online. It was shown that system shattering is eliminated. Simulation results indicated that this method possesses the advantages...
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