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An enhanced dead reckoning algorithm with hybrid extrapolation models(AisaSim 2016)

International Journal of Modeling, Simulation, and Scientific Computing 2017年第2期8卷 153-166页

作者： Dong Meng Yi-Ping Ya Feng Yao College of Information System and Management National University of Defense TechnologyChangshaHunanP.R.China

The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion *** paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive *** proposed algorithm perform extrapolation using a number of candidate *** idea is based on the assumption that a complex trajectory can be decomposed into several simple *** experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.

关键词： Dead reckoning algorithm extrapolation models motion functions prediction accuracy trajectory reconstruction.

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Computer Aided Measurement of Smart City Multi-Source Information Synergy Structure Dependent on Adaptive Artificial Immune Network Algorithm

Computer Aided Measurement of Smart City Multi-Source Inform...

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作者： Yongliang Yang Department of Information Engineering Shandong Vocational College of Light Industry

In view of the low efficiency in the measurement of multi-source information synergy structure for the smart city and other problems,a kind of computer aided measurement of smart city multi-source information synergy structure（hereinafter referred to as CAMMISS for short） that is dependent on the adaptive artificial immune network algorithm（hereinafter referred to as AAINA for short） is put ***,the number of the input and output nodes in the multi-source information synergy structure（hereinafter referred to as MSISS for short） is determined in accordance with the number of the situation input index and the value of the output *** connection weight value and the threshold value of each layer are randomly initialized,and the floating number encoding method is used to encode the weight value and the threshold value into the adaptive artificial immune network *** experimental results show that compared with the methods of the MSISS and the genetic algorithm for the optimization of the multi-source information,the method put forward in this paper has relatively fast convergence speed and relatively high prediction accuracy.

关键词： Smart City Multi-source Information Synergy Structure Adaptive Artificial Immune Network Algorithm Multi-source Information prediction accuracy

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Error Correction of Support Vector Regression Model for Copper-Matte Converting Process

Error Correction of Support Vector Regression Model for Copp...

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2015年中国智能自动化学术会议

作者： Jun Chen Xiaoqi Peng Xiuming Tang School of Information Science and Engineering Central South University Institute of Information and Electrical Engineering Hunan University of Science and Technology

To improve the performance of copper-matte Peirce-Smith Converting(PSC), the influence of local process data to e-support vector regression(SVR)model for converting process is studied. This paper proposes an Error Correction method for e-Support Vector Regression(ECVR), in which the influence of local support vector to prediction results is considered. Two ECVR models for slag weight and blowing time of S1 period(that is, the first slag producing period of PSC) are developed by the real production data. Simulation results show that ECVR model can significantly improve prediction accuracy and generalization of the converting decision variable in S1 period.

关键词： Support vector regression Error correction Copper-matte converting prediction accuracy Generalization

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Hybrid Optimization-Based GRU Neural Network for Software Reliability prediction

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国际计算机前沿大会会议论文集 2020年第2期 369-383页

作者： Maochuan Wu Junyu Lin Shouchuang Shi Long Ren Zhiwen Wang China Shipbuilding Corporation 716 Research Institute CSIC Information Technology Co.LtdLianyungangJiangsuChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina Harbin Engineering University HarbinChina

Aiming at the problems of low prediction accuracy and weak generalization ability of current reliability prediction models,this paper proposes a hybrid multi-layer heterogeneous particle swarm optimization algorithm(HMHPSO)that can simultaneously optimize the structure and parameters of the GRU neural *** first introduced a multi-layer heteromass particle swarm optimization(MHPSO)algorithm,which sets the population topology as a hierarchical structure and introduces the concept of attractors,so as to improve the update formula of particle speed,and enhance the information interaction ability between particles,increase the diversity of the groups,thereby improving the optimization ability of the *** the HMHPSO used the quantum particle swarm optimization(QPSO)algorithm to determine the structure of the GRU,that is,the number of hidden *** results show that the algorithm can generate GRU neural networks with high generalization performance and low architecture complexity,and has better prediction accuracy in software reliability prediction.

关键词： Software reliability PSO GRU prediction accuracy Generalization performance

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