Recursive learning in real time using fuzzy pattern matching

Our team of research “diagnosis of industrial processes” works on diagnosis in using classification method for data coming from industrial and medical sectors. The goal is to develop a decision-making system. We use the fuzzy pattern matching (FPM) as a method of classification and the transformation probability–possibility of Dubois and Prade to construct the densities of possibilities. These densities are used to assign the new observations to their suitable class. Sometimes we cannot have enough observations in the learning set for several reasons, especially the cost and the time. The insufficient number of observations in the learning set involves several negative effects: bad classification, inability to detect the real number of operating states, inability to know the real shapes of the classes and inability to follow their evolution. The solution is to increase our knowledge about the system in accumulating the information obtained from each classified observation. This solution called incremental learning needs to remake the learning process after the classification of each new observation. This incremental learning must be made in real time to take the advantage of the information added by each new classified point. When the number of points in the learning set increases, the time needed to do the learning process also increases, which makes the incremental learning in real time difficult. In this paper, we recall the principle of the FPM algorithm. Then we show how we can include the incremental learning in this method, and we compare the obtained computing times with the ones of classical method. To conclude we expose the advantages of such learning in real time.