Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

Part-of-speech (POS) tagging is a basic problem that needs to be solved in the informationization of Chinese-Hmong mixed text including square Hmong characters, so far no one has studied it. This paper proposes a POS tagging approach for Chinese-Hmong mixed text by utilizing improved Hidden Markov Model (HMM) to expand contextual information. The results of comparative experiments based on cross-validation reveal the proposed approach has perfect performance, and is able to obtain tagging results with good consistency and high coverage.