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

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 5x2 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. •Adaptive Neuro-Fuzzy Inference System (ANFIS) is a hybrid learning algorithm that augments the learning ability of neural networks to fuzzy inference systems.•Though ANFIS is effectively applied to several real-world problems, effective parameter optimization remains a challenge. This paper aims at presenting a new innovative approach for parameter optimization of ANFIS that has competitive computational performance and acceptable efficacy.•The novel approach presented in this paper works under supervised settings using a combination of probabilistic mixture models and perceptron-based learning for the parameterization of ANFIS.•The proposed algorithm learns the ANFIS membership functions by generating a mixture of the finite number of probability distributions against each input feature.•