基于Vague集及隐马尔科夫的大学生心理危机预测

现有关于大学生心理问题的研究主要停留在心理健康教育层面,对心理问题的发现缺乏有效手段,难以准确预知大学生心理危机.针对这一问题,应用隐马尔科夫模型对大学生心理危机进行预测,分析影响大学生心理健康的因素,并利用Vague集理论构造核心因素集;确立隐马尔科夫模型各个要素的初始值,建立预测大学生心理危机的模型,运用Baum-Welch算法对模型参数进行训练,并结合Viterbi算法进行预测.根据某大学学生心理健康评估原始记录进行实例分析,验证了模型的正确性.研究结果表明,采用核心因素集能够提高预测的准确性,为预知大学生心理问题提供有效的途径.

Prediction of College Students' Psychological Crisis Based on Vague Set and HMM

Existing research on college students' psychological problems mainly stays at the mental health education level.The analysis methods and corresponding treatment measures of psychological problems have been sometimes proposed.However, it is still difficult to accurately predict the students' psychological crisis, and is lacking in effective means to discover these problems.To settle this, a hidden Markov model (HMM) was applied to predict the psychological crisis of college students.A core factor set which may lead to crisis was constructed based on the theory of vague set, and the factors that affect the mental health of college students were analyzed.An initial HMM to predict college students psychological crisis was built by figuring out the initial value of each element of the model, and trained by using the Baum-Welch algorithm.Combining with the Viterbi algorithm, the prediction was carried out, and its correctness was verified by instance analysis according to the original students' psychological health valuation records in an university.The results show that the method can improve the prediction accuracy by adopting the core factor set in the model which would be an effective means to foresee the hidden crisis.