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In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min–Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree. [ABSTRACT FROM AUTHOR]

In this paper, classification of ball bearing faults using vibration signals is presented. A review of condition monitoring using vibration signals with various intelligent systems is first presented. A hybrid intelligent model, FMM-RF, consisting of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model, is proposed. A benchmark problem is tested to evaluate the practicality of the FMM-RF model. The proposed model is then applied to a real-world dataset. In both cases, power spectrum and sample entropy features are used for classification. Results from both experiments show good accuracy achieved by the proposed FMM-RF model. In addition, a set of explanatory rules in the form of a decision tree is extracted to justify the predictions. The outcomes indicate the usefulness of FMM-RF in performing classification of ball bearing faults. [ABSTRACT FROM AUTHOR]

• An improved multi-agent system based on Fuzzy ARTMAP (FAM) and Q-learning, i.e., IQ-MACS, is proposed. • A trust measurement using a combination of Q-learning and Bayesian formalism is added at team level. • Two real-world case studies, i.e., human motion detection and motor fault detection, with and without noise are used to evaluate the proposed model. • The outcome indicates that IQMACS is able to produce promising results. The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods. [ABSTRACT FROM AUTHOR]