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Stereo garage parking space allocation model and simulation analysis

Journal of Measurement Science and Instrumentation 2019年第4期10卷 369-378页

作者： WANG Xiao-nong LI Jian-guo HE Yun-peng School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking *** gray neural network is used to forecast the stay time of the vehicle and particle swarm optimization algorithm is used to allocate the parking spaces in the stereo *** proposed stereo garage mathematical model is established on condition that vehicle arrival interval obeys Poisson *** performance of stereo garage is evaluated by the average waiting time,average waiting queue length,average service time and average energy consumption of the *** comparing the efficiency indexes of the existing model based on near-distribution principle and the proposed model based on gray neural network and particle swarm algorithm,it is proved that the proposed model based on gray neural network and particle swarm algorithm is effective in improving the efficiency of garage operation and reducing the energy consumption of garage.

关键词： stereo garage parking space allocation particle swarm algorithm grey neural network algorithm near-distribution principle

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Bearing Fault Diagnosis Based on Optimised Stacked Auto-Encoders and Fractional-order Gradient Descent with Momentum BP Neural Network

Bearing Fault Diagnosis Based on Optimised Stacked Auto-Enco...

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第43届中国控制会议

作者： Xinyi Lei Sai Li Zhixia Ding Le Yang Yong Zhang School of Electrical and Information Engineering Wuhan Institute of Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology

The health condition of bearings determines whether mechanical equipment can operate normally,and it is very important for bearings to carry out timely fault identification and *** process of bearing diagnosis can be improved mainly from feature extraction and fault classification *** this paper,an innovative approach for feature selection and fault diagnosis is proposed,utilizing a bearing fault diagnosis method that employs an optimized stacked auto-encoder combined with a momentum fractional-order BP neural *** approach firstly uses stacked auto-encoder optimised by particle swarm algorithm to select the deep features in time and frequency domains respectively,and then fuses the selected deep features to build the fault feature ***,in order to solve the problem of slow convergence speed of traditional BP neural network,a momentum fractional order BP neural network is established to achieve fault *** results from the experiments indicate that the developed approach exhibits a notable advancement in characterizing bearing features,and enhanced classification accuracy and efficiency.

关键词： Fault Diagnosis Auto-Encoder particle swarm algorithm Fractional-order gradient Momentum gradient

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Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

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Agricultural Science & Technology 2009年第6期10卷 135-137,154页

作者：曹志勇邱靖曹志娟杨毅云南农业大学基础与信息工程学院云南昆明650201 云南财经大学信息学院云南昆明650221

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

关键词： Backward propagation neural network particle swarm algorithm Plant diseases and pests Early-warning model

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