基于改进Elman网络模型的软件可靠性预测

	基于改进Elman网络模型的软件可靠性预测
	程绪超; 陈新宇; 郭平
	2011
发表期刊	通信学报
ISSN	1000436X
卷号	32 期号:4 页码:86-93
摘要	为提高神经网络模型对软件可靠性预测结果的准确性和可信性,提出了一种基于多目标优化算法改进Elman网络模型Mop-IElman(multi-objective optimization-based improved Elman neural network)的方法:1)在Elman网络基础上,设计输出层的延迟反馈层,作为另一个状态层;2)以网络的结构和2个状态层的初始输出值为网络配置的变量,以网络的预测精度和顽健性为目标,采用NSGA-II(non-dominated sorting genetic algorithm II)进行多目标优化得到帕累托解,最大化网络预测精度与顽健性之和从而确定网络配置。通过两组实际软件失效数据对Mop-IElman进行实验验证,并与前馈网络、Elman网络、单目标优化Elman网络以及多目标优化Elman网络进行比较研究,结果表明Mop-IElman的预测结果具有较高的准确性和可信性。
其他摘要	In order to improve accuracy and dependability of using neural network for software reliability prediction, a multi-objective optimization-based improved Elman recurrent network method (Mop-IElman) was proposed. First, on the basis of the Elman network, a self-delay feedback of the output layer as another context layer was designed. Second, the network architecture and the initial outputs of these two context layers were taken as variables of network configuration setting, and NSGA-II was employed to simultaneously optimize prediction performance and robustness, then the Pareto solution was obtained. After that, by maximizing the sum of prediction performance and robustness, the final network configuration setting was determined. Finally, the proposed method was compared with the feed-forward neural network, the Elman network, both the single-objective and the multi-objective optimization Elman networks with respect to two real software failure data. It demonstrated that the proposed Mop-IElman achieves higher prediction accuracy and dependability.
关键词	软件可靠性预测可信性反馈神经网络 Nsga-ii 多目标优化computer Software Selection And Evaluation Forecasting Multiobjective Optimization Network Architecture Neural Networks
部门归属	北京师范大学图形图像与模式识别实验室;中国科学院软件所计算机科学国家重点实验室
语种	中文
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/13899
专题	基础软件与系统重点实验室
推荐引用方式 GB/T 7714	程绪超,陈新宇,郭平. 基于改进Elman网络模型的软件可靠性预测[J]. 通信学报,2011,32(4):86-93.
APA	程绪超,陈新宇,&郭平.(2011).基于改进Elman网络模型的软件可靠性预测.通信学报,32(4),86-93.
MLA	程绪超,et al."基于改进Elman网络模型的软件可靠性预测".通信学报 32.4(2011):86-93.

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