针对传统和声算法收敛速度慢和搜索精度低等固有缺点,提出一种改进的自适应全局最优和声搜索算法.在即兴创作方案中,带宽由当前和声里的最优和声变量和最差和声变量之差表示,使得带宽具有针对具体情况的自适应能力,并且每次保存最优和声中一个随机和声变量.在产生的随机数大于和声记忆库存储考虑概率时,利用种群内差分随机生成一个和声变量.为了提高和声搜索算法的搜索能力,在即兴创作结束后产生一个新的和声的同时,再从当前种群中的最小和声到最大和声之间随机产生一个和声,然后将两个新产生和声中误差小的和声进入更新和声记忆库阶段.将所提出的算法与3个改进和声搜索算法在13个测试函数上进行对比.试验结果表明,提出的改进算法具有更好的全局搜索能力和收敛速度.

为了进一步提高中文语料库中语料的词性标注效率,在分析最大熵模型（MEM）和隐马尔科夫模型（HMM）所涉及理论、算法及其在中文词性标注技术中的应用的基础上,进行了基于MEM和HMM的中文词性标注实验.实验结果显示,基于MEM和HMM的中文词性标注算法都获得了一致性很好且覆盖率较高的标注效果,中文词性标注的准确率、召回率和F1这3个指标均达到92%以上;MEM的标注效果总体上比HMM的稍佳.

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.