准确预测处理器性能对计算机硬件设计与改进有着重要意义。然而,处理器预测系统存在两个核心问题:预测过程中处理器内部构造复杂和不确定性以及预测结果的不可解释性。置信规则库作为一种基于IF-THEN规则的建模方法,具有一定的可解释性...准确预测处理器性能对计算机硬件设计与改进有着重要意义。然而,处理器预测系统存在两个核心问题:预测过程中处理器内部构造复杂和不确定性以及预测结果的不可解释性。置信规则库作为一种基于IF-THEN规则的建模方法,具有一定的可解释性并且可以处理复杂系统评估与预测中的不确定信息。但BRB的规则爆炸问题限制了专家知识的使用。因此,本文提出了一种基于近似置信规则库(ABRB)的处理器性能预测模型。该模型通过构建单属性BRB模型来解决规则爆炸问题,并通过基于投影协方差矩阵自适应进化策略(P-CMA-ES)算法对专家知识给定的初始参数进行优化。最后以UCI中处理器数据集为例,验证了所提方法的有效性。Accurate prediction of processor performance is important for computer hardware design and improvement. However, there are two core problems in processor prediction systems: the complexity and uncertainty of processor internals during the prediction process and the non-interpretability of the prediction results. Belief rule base (BRB), as a modelling method based on IF-THEN rules, has some interpretability and can handle uncertain information in the evaluation and prediction of complex systems. However, the rule explosion problem of BRB limits the use of expert knowledge. Therefore, this paper proposes a processor performance prediction model based on approximate belief rule base. The model solves the rule explosion problem by constructing a single-attribute BRB model and optimizes the initial parameters given by the expert knowledge by the Projection Covariance Matrix Adaptive Evolutionary Strategy (P-CMA-ES) based algorithm. Finally, the effectiveness of the proposed method is validated using the UCI mid-processor dataset as an example.展开更多
可靠的粮堆温度预测对于粮食安全存储影响重大,以可解释的方法对粮堆的温度进行预测可以提高预测结果的可靠性。由于在建模过程中,BRB (Belief Rule Base)的可解释性可能被削弱或丧失。因此,提出了一种新的基于主成分分析和可解释性置...可靠的粮堆温度预测对于粮食安全存储影响重大,以可解释的方法对粮堆的温度进行预测可以提高预测结果的可靠性。由于在建模过程中,BRB (Belief Rule Base)的可解释性可能被削弱或丧失。因此,提出了一种新的基于主成分分析和可解释性置信规则库粮堆温度预测模型(Principal Components Analysis, Interpretable Belief Rule Base, PCA-IBRB)。首先,通过PCA将影响粮堆温度的主要指标筛选出来,根据筛选出的指标并结合粮堆温度特征对模型的可解释性建模准则进行定义。其次,利用ER (Evidential Reasoning)对模型的结果进行推理。然后,提出了一种新的带有可解释性约束的投影协方差矩阵自适应进化策略(Projection Covariance Matrix Adaptation Evolutionary Strategies, P-CMA-ES)算法,来保证优化过程的可解释性;最后,通过对吉林省某粮仓的实测数据进行了粮堆温度预测实验研究,平均MSE值达到了0.0044,验证了模型在粮堆温度预测中的有效性。The reliable temperature prediction of grain heap has great influence on safe storage of grain. Grain heap temperature prediction by interpretable method can improve the reliability of prediction results. Since the interpretability of Belief Rule Base may be weakened or lost during the modeling process. Therefore, a new reservoir temperature prediction model based on Principal Components Analysis and Interpretable Belief Rule Base (PCA-IBRB) is proposed. First, the main indicators affecting the temperature of the grain reactor were selected by PCA, and the interpretability modeling criteria of the model were defined according to the selected indicators and the grain reactor temperature characteristics. Secondly, the Evidential Reasoning (ER) method, as a transparent reasoning engine, ensures the interpretability of the reasoning process. Then, a new Projection Covariance Matrix Adaptation Evolutionary Strategies (P-CMA-ES) algorithm with interpretability constraint is proposed to ensure the interpretability of the optimization process. Finally, the temperature prediction of grain reactor was studied by the measured data, and the average MSE value reached 0.0044, which verified the effectiveness of the model in the temperature prediction of grain reactor.展开更多
文摘准确预测处理器性能对计算机硬件设计与改进有着重要意义。然而,处理器预测系统存在两个核心问题:预测过程中处理器内部构造复杂和不确定性以及预测结果的不可解释性。置信规则库作为一种基于IF-THEN规则的建模方法,具有一定的可解释性并且可以处理复杂系统评估与预测中的不确定信息。但BRB的规则爆炸问题限制了专家知识的使用。因此,本文提出了一种基于近似置信规则库(ABRB)的处理器性能预测模型。该模型通过构建单属性BRB模型来解决规则爆炸问题,并通过基于投影协方差矩阵自适应进化策略(P-CMA-ES)算法对专家知识给定的初始参数进行优化。最后以UCI中处理器数据集为例,验证了所提方法的有效性。Accurate prediction of processor performance is important for computer hardware design and improvement. However, there are two core problems in processor prediction systems: the complexity and uncertainty of processor internals during the prediction process and the non-interpretability of the prediction results. Belief rule base (BRB), as a modelling method based on IF-THEN rules, has some interpretability and can handle uncertain information in the evaluation and prediction of complex systems. However, the rule explosion problem of BRB limits the use of expert knowledge. Therefore, this paper proposes a processor performance prediction model based on approximate belief rule base. The model solves the rule explosion problem by constructing a single-attribute BRB model and optimizes the initial parameters given by the expert knowledge by the Projection Covariance Matrix Adaptive Evolutionary Strategy (P-CMA-ES) based algorithm. Finally, the effectiveness of the proposed method is validated using the UCI mid-processor dataset as an example.
文摘可靠的粮堆温度预测对于粮食安全存储影响重大,以可解释的方法对粮堆的温度进行预测可以提高预测结果的可靠性。由于在建模过程中,BRB (Belief Rule Base)的可解释性可能被削弱或丧失。因此,提出了一种新的基于主成分分析和可解释性置信规则库粮堆温度预测模型(Principal Components Analysis, Interpretable Belief Rule Base, PCA-IBRB)。首先,通过PCA将影响粮堆温度的主要指标筛选出来,根据筛选出的指标并结合粮堆温度特征对模型的可解释性建模准则进行定义。其次,利用ER (Evidential Reasoning)对模型的结果进行推理。然后,提出了一种新的带有可解释性约束的投影协方差矩阵自适应进化策略(Projection Covariance Matrix Adaptation Evolutionary Strategies, P-CMA-ES)算法,来保证优化过程的可解释性;最后,通过对吉林省某粮仓的实测数据进行了粮堆温度预测实验研究,平均MSE值达到了0.0044,验证了模型在粮堆温度预测中的有效性。The reliable temperature prediction of grain heap has great influence on safe storage of grain. Grain heap temperature prediction by interpretable method can improve the reliability of prediction results. Since the interpretability of Belief Rule Base may be weakened or lost during the modeling process. Therefore, a new reservoir temperature prediction model based on Principal Components Analysis and Interpretable Belief Rule Base (PCA-IBRB) is proposed. First, the main indicators affecting the temperature of the grain reactor were selected by PCA, and the interpretability modeling criteria of the model were defined according to the selected indicators and the grain reactor temperature characteristics. Secondly, the Evidential Reasoning (ER) method, as a transparent reasoning engine, ensures the interpretability of the reasoning process. Then, a new Projection Covariance Matrix Adaptation Evolutionary Strategies (P-CMA-ES) algorithm with interpretability constraint is proposed to ensure the interpretability of the optimization process. Finally, the temperature prediction of grain reactor was studied by the measured data, and the average MSE value reached 0.0044, which verified the effectiveness of the model in the temperature prediction of grain reactor.
