在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learn...在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.展开更多
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
混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Be...混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Belief Rule Base)的混凝土抗压强度预测方法。该方法首先利用随机森林(RF)算法得出部分指标的重要度,利用证据推理算法赋权融合各项指标。其次利用置信规则库专家系统将混凝土抗压指标中定性知识与定量的数据相结合,建立置信规则库预测模型。然后采用投影协方差矩阵自适应进化策略算法(P-CMA-ES)优化模型的参数。最后通过UCI数据库混凝土抗压强度数据集,对提出的方法进行了验证。实验结果表明,该方法保证了模型推理的透明,本文提出的预测方法具有较高的精度且具有一定的可解释性。The compressive strength of concrete directly affects the safety and stability of buildings, and the traditional concrete compressive strength prediction method has a long period and high cost. In order to solve the problems of material composition and complex analysis process faced by concrete compressive strength analysis, a concrete compressive strength prediction method based on Evidential Reasoning (ER) and Belief Rule Base was proposed. Firstly, the random forest (RF) algorithm is used to obtain the importance of some indicators, and the evidence inference algorithm is used to empower and fuse various indicators. Secondly, the confidence rule base expert system is used to combine the qualitative knowledge and quantitative data in the concrete compressive index, and the confidence rule base prediction model is established. Then, the projection covariance matrix adaptive evolution strategy algorithm (P-CMA-ES) was used to optimize the parameters of the model. Finally, the proposed method is verified by the concrete compressive strength dataset of the UCI database. Experimental results show that the proposed method ensures the transparency of model reasoning, and the prediction method proposed in this paper has high accuracy and interpretability.展开更多
文摘在电力系统中,针对用于解决多种燃料方案经济调度(economic dispatch,ED)算法收敛精度低的问题,提出了基于动态反向学习的协方差矩阵自适应进化策略(covariance matrix adaptation evolutionary strategy with dynamic opposition learning,CMA-DOL),旨在根据样本点的变化动态更新反向样本点的范围,提高样本多样性,防止陷入局部最优.本方法在分别由10、40、80个发电机组组成的3个测试系统上进行了验证,并与文献中的其他算法进行比较,对超过50次独立运行的结果进行统计度量,实验结果表明CMA-DOL可以获得更好的解决方案.
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.
文摘混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Belief Rule Base)的混凝土抗压强度预测方法。该方法首先利用随机森林(RF)算法得出部分指标的重要度,利用证据推理算法赋权融合各项指标。其次利用置信规则库专家系统将混凝土抗压指标中定性知识与定量的数据相结合,建立置信规则库预测模型。然后采用投影协方差矩阵自适应进化策略算法(P-CMA-ES)优化模型的参数。最后通过UCI数据库混凝土抗压强度数据集,对提出的方法进行了验证。实验结果表明,该方法保证了模型推理的透明,本文提出的预测方法具有较高的精度且具有一定的可解释性。The compressive strength of concrete directly affects the safety and stability of buildings, and the traditional concrete compressive strength prediction method has a long period and high cost. In order to solve the problems of material composition and complex analysis process faced by concrete compressive strength analysis, a concrete compressive strength prediction method based on Evidential Reasoning (ER) and Belief Rule Base was proposed. Firstly, the random forest (RF) algorithm is used to obtain the importance of some indicators, and the evidence inference algorithm is used to empower and fuse various indicators. Secondly, the confidence rule base expert system is used to combine the qualitative knowledge and quantitative data in the concrete compressive index, and the confidence rule base prediction model is established. Then, the projection covariance matrix adaptive evolution strategy algorithm (P-CMA-ES) was used to optimize the parameters of the model. Finally, the proposed method is verified by the concrete compressive strength dataset of the UCI database. Experimental results show that the proposed method ensures the transparency of model reasoning, and the prediction method proposed in this paper has high accuracy and interpretability.
