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2009中国控制与决策会议

作者： GAO Xian-zhong,HOU Zhong-xi,REN Bo-tao College of aerospace and material engineering,National University of Defense Technology,Changsha 410073

＜正＞Aiming to solve the problem of unknown parameters estimation in nonlinear and/or non-Gaussian dynamic system,Extension Particle Filtering（EPF） algorithm was *** algorithm modeled unknown parameters by Gaussian random walk process,regarded unknown parameters in dynamic system as a part of state variations,and then estimated the state variations in extension nonlinear dynamic system by particle filtering *** order to improve the estimate precision of unknown parameters by utilizing observable information effectively,a new important density was purposed to instead of Bootstrap filter,further more,it avoided transformation about *** order to solve the problem that covariance augmented infinitely with time in Gaussian random walk model,and Kernel smooth factor restrained covariance excessively so as to the values of estimate parameters could not access the values of true parameters sufficiently,the Gradually Reduce（GR） factor was purposed to instead of Kernel *** the end of this paper,the effectiveness and availability of purposed algorithm was validated by an exemplum.

关键词： Particle Filtering algorithm Parameters estimation Important density smooth factor

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An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm

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Electrical Science & Engineering 2020年第1期2卷 4-10页

作者： Zhida Guo Jingyuan Fu School of Economics and Management Dalian Jiaotong UniversityDalianChina Academic Unit of Human Communication Developmentand Information SciencesFaculty of educationthe University of Hong KongHong KongChina

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change *** the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of *** algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in *** the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into *** target values were obtained after the calculation of the predicted ***,compared with the results of GRNN,the improved method realized higher prediction *** thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

关键词： Carbon emissions Genetic Algorithm Generalized Regression Neural Network smooth factor Prediction

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