Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

被引:14
作者
Marzi, Hosein [1 ]
Darwish, Ahmed Haj [2 ]
Helfawi, Humam [3 ]
机构
[1] St Francis Xavier Univ, Dept Informat Syst, Antigonish, NS, Canada
[2] Univ Aleppo, Fac Informat Engn, Dept Artificial Intelligence & Nat Languages, Aleppo, Syria
[3] Univ Aleppo, Fac Informat Engn, Aleppo, Syria
关键词
Bees Algorithm; ANFIS; Fuzzy Systems; Hybrid Learning; NEURAL-NETWORK; FUZZY; SYSTEM;
D O I
10.1080/10798587.2016.1196880
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:227 / 234
页数:8
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