基于HMM的自适应软件决策模型

为实现软件的自适应,针对复杂多变的运行环境,提出一个基于隐Markov模型(HMM)的自适应软件决策模型.首先运用高斯混合模型(GMM)对初始环境进行分类,然后使用softmax回归对感知环境进行归类划分处理,最后利用HMM代替人工干预进行软件决策.实验结果表明,该自适应软件模型在感知环境发生变化的条件下,能很好地实现软件自适应决策.

Adaptive Software Decision Model Based on HMM

In order to realize the adaptive software, we proposed an adaptive software decision model based on hidden Markov model(HMM) for the complex and changeable operating environment.Firstly, the Gaussian mixture model (GMM) was used to classify the initial environment. Secondly, the softmax regression was used to classify and divide the perceptual environment. Finally, we used HMM instead of the manual intervention to make software decisions. The experimental results show that the adaptive software model can achieve the adaptive software decision well under the condition of the change of the perceptual environment.