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.

神经模糊模型已应用于各个领域，其中优化参数耗时长，特征提取模糊方法创新较少的问题仍有待解决。在这里，我们提出了一种新颖的循环强化层次模型（CRHM），用于有效和高效的识别。CRHM的创新策略包括层次结构、模糊子系统组和循环机制。创新构建层次结构提取特征，语义上将低级特征转化为高级特征，在每个隐藏层采用模糊子系统组作为特征提取单元，保证了特征的多样性，避免了模糊规则爆炸，并减少聚类时间。循环机制首先被提出，将层次结构和输出层直接连接起来，一次又一次地传递调整后的参数，逐步强化特征。为了证明 CRHM 的性能，我们在基准一维和二维数据集上与几种最先进的算法进行了广泛的比较。实验结果表明，CRHM 的识别率高于卷积神经网络 (CNN)，而训练时间仅为 CNN 的 5%，这证实了我们的方法提供了一种新的识别模型，可以同时提高识别的有效性和无需先进设备即可提高效率。此外，