Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network

Yang, Jingfeng; Zhang, Nanfeng; Li, Ming; Zheng, Yanwei; Wang, Li; Li, Yong; Yang, Ji; Xiang, Yifei; Luo, Liang

doi:10.1155/2018/1067927

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…To make the total length of the source text file the shortest, we need to determine the encoding method , which makes the value of ∑ =0 minimum. This Huffman encoding is based on the Huffman tree structure, and the Huffman tree is constructed as below [3,4,21].…”

Section: Optimized Huffman Encoding Methodmentioning

confidence: 99%

“…Therefore, the Huffman encoding method is very suitable for vehicle information data with higher identification requirements. In order to solve the practical problems of traditional Huffman coding, such as large buffer and high complexity, this paper improves the Huffman tree structure by using a maximum entropy neural network [21,22]. The main steps are as follows:…”

Section: Optimized Huffman Encoding Methodmentioning

confidence: 99%

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Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms

Yang

Zhang

et al. 2019

Complexity

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Because of the continuous progress of vehicle hardware, the condition where the vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress of vehicle hardware, the number of texts shows exponential growth in actual operation. In order to optimize the efficiency of mass data transmission in actual operation, this paper presented the text information (including position information) of the maximum entropy principle of a neural network probability prediction model combined with the optimized Huffman encoding algorithm, optimization from the exchange of data to data compression, transmission, and decompression of the whole process. The test results show that the text type vehicle information based on compressed algorithm to optimize the algorithm of data compression and transmission can effectively realize data compression. It can also achieve a higher compression rate and data transmission integrity, and after decompression it can basically guarantee no distortion. The method proposed in this paper is of great significance for improving the transmission efficiency of vehicle text information, improving the interpretability and integrity of text information, realizing vehicle monitoring, and grasping real-time traffic conditions.

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Section: Optimized Huffman Encoding Methodmentioning

confidence: 99%

Section: Optimized Huffman Encoding Methodmentioning

confidence: 99%

Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms

Yang

Zhang

et al. 2019

Complexity

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“…The RBFNN has strong nonlinear approximation ability but simple network architecture. Also, it has a fast learning speed and the hidden layer input and output matrices have a linear relationship, which makes it an ideal algorithm for calculating the degree of influence (Yang et al, 2018;Wang et al, 2018;Yang et al, 2021;Zhao and Liu, 2021).…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…In supervised learning, the determined hidden layer parameters and the least square method are used to calculate the weights of the hidden layer and the output layer. The output of the i-th hidden layer of the RBF neural network is expressed as (Yang et al, 2018;Wang et al, 2018;Yang et al, 2021;Zhao and Liu, 2021):…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Song

Zhou

Chang³

et al. 2021

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Purpose Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc. Design/methodology/approach Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed. Findings The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments. Originality/value In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

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“…e output matrix of the hidden layer after iterative convergence has a linear relationship with the output. It is an ideal algorithm for calculating the degree of influence [19,20].…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network

et al. 2020

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In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

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Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network

Cited by 7 publications

References 21 publications

Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms

Vehicle Text Data Compression and Transmission Method Based on Maximum Entropy Neural Network and Optimized Huffman Encoding Algorithms

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network

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