LAUSR

Abstract Neuro-fuzzy models have been applied in various domains, in which the issue of long time-consumption for optimizing parameters and less innovation in fuzzy method for feature extraction remains to be solved. Here, we present a novel cycle reinforce hierarchical model (CRHM) for effective and efficient recognition. The innovative strategies of CRHM consist of the hierarchical structure, the groups of fuzzy subsystems and the cycle mechanism. The hierarchical structure is innovatively built to extract features and transform the low-level features into advanced ones semantically, in which we adopt the groups of fuzzy subsystems as feature extraction units in each hidden layer, which ensures the diversity of features, avoids the fuzzy rules explosion, and reduces the time for clustering. The cycle mechanism is first proposed to connect the hierarchical structure and the output layer directly, transferring the tuned parameters again and again, to reinforce features gradually. To demonstrate the performance of CRHM, we have conducted extensive comparison with several state-of-the-art algorithms on benchmark 1D and 2D datasets. The experimental results show that the recognition rate of CRHM is higher than convolutional neural network (CNN), while the training time is only 5% of CNN's, which confirms that our approach provides a novel model for recognition, which can simultaneously improve the effectiveness and efficiency without the need of advanced equipment. In addition, the analysis results about the contribution of the core strategies to CRHM performance indicates that the contribution of the hierarchical structure is greater than that of the groups of fuzzy subsystems, which is superior than that of the cycle mechanism.