基于改进隐马尔科夫模型的鲁棒用户行为识别

提出了一种基于改进隐马尔科夫模型的用户行为识别方法.采用遗传算法用于优化隐马尔科夫模型的初始参数,将混沌算子代替遗传算法中高斯变异算子,以避免传统遗传算法在收敛过程中的停滞和早熟问题,并有效解决传统隐马尔科夫模型中Baum-Welch算法对初始参数敏感的问题.此外,采用UCI中ADLs数据对用户行为进行识别,实验结果表明该方法具有很高的识别率和可靠性.
An improved Hidden Markov Models (HMM) was proposed to recognize the user's behavior. In order to improve the learning efficiency of Baum-Welch algorithm in HMM, and to solve the problem of initial sensitivity, the improved GA s used to optimize the initial parameters of HMM, in which the Chaos operator is utilized to avoid the problem of stagnation and premature convergence of the traditional GA in the convergence process. Finally, the experiment results based on ADLs data in UCI show the algorithm's availability and reliability for user's behavior recognition.