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Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

机译：使用增强的Bees算法和最小二乘估计训练ANFIS

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摘要

This paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative; therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines Bees Algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the training process of ANFIS. The experimental results validate high generalization capabilities of the BA-LSE training algorithm in comparison to the original hybrid training model of ANFIS. The new training model also enhances local minima avoidance and has high convergence rate.
机译：本文介绍了为自适应神经模糊推理系统（ANFIS）开发新型训练模型的研究结果。 ANFIS将人工神经网络的学习能力与Takagi-Sugeno模糊推理系统集成在一起，以近似非线性函数。因此，它被视为通用估计器。 ANFIS训练过程中使用的原始算法具有混合模型，该模型使用最陡峭体面导数；因此，它在训练过程中继承了较低的收敛速度和局部最小值。在这项研究中，提出了一种结合了Bees算法（BA）和最小二乘估计（LSE）（BA-LSE）的训练算法。与LSE的最佳解决方案集成在一起，对BA的本地和全球探索改善了ANFIS培训过程的当前缺陷。在函数逼近，时间序列预测和分类实验的三种不同情况下，对提出的训练算法进行了检验，以验证ANFIS训练过程中的有希望的改进。与原始的ANFIS混合训练模型相比，实验结果验证了BA-LSE训练算法的高泛化能力。新的训练模型还增强了局部最小规避，并且具有较高的收敛速度。

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