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Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

机译:使用模拟退火算法训练ANFIS结构以进行动态系统识别

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In this paper, a new method is presented for the training of the Adaptive Neuro-Fuzzy Inference System (ANFIS). In this work, it is ensured that the best model is created by optimising the premise and consequent parameters of ANFIS by using Simulating Annealing (SA) based on an iterative algorithm. The proposed method was applied to dynamic system identification problems. The simulation results of the proposed method are compared with the Genetic algorithm (GA), Backpropagation (BP) algorithm and different methods from the literature. At the end of this study it was found that the optimisation of ANFIS parameters is more successful by using SA than by GA, BP and the other methods. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文提出了一种新的自适应神经模糊推理系统(ANFIS)的训练方法。在这项工作中,通过使用基于迭代算法的模拟退火(SA),可以通过优化ANFIS的前提和后续参数来确保创建最佳模型。将该方法应用于动态系统辨识问题。将该算法的仿真结果与遗传算法(GA),反向传播(BP)算法以及文献中不同的方法进行了比较。在研究的最后,发现使用SA进行ANFIS参数的优化比使用GA,BP和其他方法更为成功。 (C)2018 Elsevier B.V.保留所有权利。

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