ANFIS learning using expectation maximization based Gaussian mixture model and multilayer perceptron learning

doi:10.1016/j.asoc.2023.110958

2023-12-01

PRE

Mubasher Baig

Mian Muhammad Awais *

DOI：10.1016/j.asoc.2023.110958

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摘要

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The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid learning algorithm that combines the learning ability of neural networks with fuzzy inference systems. While ANFIS has been successfully applied to several real-world problems, effective parameter optimization remains a challenge. This paper presents a novel approach for optimizing parameters of ANFIS in supervised settings. The proposed approach utilizes a combination of probabilistic mixture models and perceptron-based learning to parameterize ANFIS. To learn ANFIS membership functions, it generates a mixture of the finite probability distributions for each input feature. Then the consequent parameters are learned by transforming them into weights of a multi layer perceptron instance. The effectiveness of the proposed method is evaluated on classification problems of varying complexity, ranging from binary-class to multi-class with variable dimensions. The results show that the proposed algorithm improves ANFIS performance in terms of accuracy rate and speed (train-time) analysis. The effectiveness and efficiency of the proposed approach have been further confirmed using a 5 x 2 cross-validation paired significance t-test. In particular, for binary-class problems, our model outperformed standard methods by up to 10% accuracy improvement. Similarly, for multi-class problems, our model achieved an average increase of 8% in accuracy while reaching up to 14% improvement. On average, the proposed model showed a reduction of about 75% in training time compared to the other models. Overall, the proposed approach offers competitive computational performance and acceptable efficacy for parameter optimization of ANFIS.

Keyword:

ANFIS parameter optimization

Expectation Maximization (EM)

Gaussian Mixture Model (GMM)

Perceptron learning

Neuro-fuzzy estimation