Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hy...Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.展开更多
针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法...针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51379162)the Water Conservancy Science and Technology Innovation Project of Guangdong Province(Grant No.2016-06)
文摘Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.
文摘针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷,结合动态加速常数协同惯性权重的粒子群(Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight,PSODACCIW)算法的全局优化能力和加权融合理论,提出基于PSODACCIW-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量,然后采用PSODACCIW算法优化诊断融合权值矩阵,最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明,该方法能够有效地应用于滚动轴承的智能检测中。
