scholarly journals Rampway length, driving safety influence, velocity, fuzzy rules, neural network

2019 ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Rules ◽  
Driving Safety

scholarly journals FMCNN: A Factorization Machine Combined Neural Network for Driving Safety Prediction in Vehicular Communication

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 11698-11706 ◽  
Author(s):  
Haitao Zhao ◽  
Tianqi Mao ◽  
Jiaxiu Duan ◽  
Yufeng Wang ◽  
Hongbo Zhu
Keyword(s):  
Neural Network ◽  
Driving Safety ◽  
Vehicular Communication ◽  
Factorization Machine

Study on the Discrimination Method of Driving Safety States Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 6111-6114
Author(s):  
Feng Ping Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Driving Safety ◽  
Assessment Index ◽  
Evaluation Indexes ◽  
The Neural Network ◽  
Discrimination Model ◽  
Assessment Index System ◽  
Discrimination Method

In order to estimating the state of driving safety and reducing accidents, a discrimination method of driving safety states based on BP neural network was presented in the paper. Firstly, the influencing factors on the vehicle driving safety were analyzed, and ten main factors that affected the driving safety of vehicles were confirmed, which constitute the safety assessment index system for vehicle driving. Then the discrimination model of driving safety states based on BP neural network was established, and inputs and outputs for the neurons were determined. At last, the input data for neurons were acquired on the basic of the main evaluation indexes of vehicle driving safety, and these data were used to train the neural network. The training result conform to expectations of the training requires.

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

2020 ◽  
Vol 17 (6) ◽  
pp. 2755-2762
Author(s):  
Pramoda Patro ◽  
Krishna Kumar ◽  
G. Suresh Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Fuzzy Neural Network ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
Approximate Function ◽  
Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

Study on Decreasing Vibration of High-Speed Train Semi-Active Suspension System

2011 ◽  
Vol 230-232 ◽  
pp. 1104-1109
Author(s):  
Zhen Ping Fan ◽  
Heng Zeng ◽  
Jian Wei Yang ◽  
Jie Li
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
High Speed ◽  
Driving Safety ◽  
High Speed Train ◽  
Proportional Valve ◽  
Joint Simulation ◽  
The Neural Network ◽  
Adaptive Control Strategy ◽  
Active Damper

Lateral semi-active damper is designed by author based on the electro-hydraulic proportional valve, from the perspective angle of improving vehicle comfort; its purpose is to ensure vehicle driving safety. At the same time, the neural network adaptive control strategy is used for joint simulation of semi-active damper. The results show that lateral semi-active damper with the train body has significantly improved compared to the traditional passive lateral damper acceleration.

Sign in / Sign up

Export Citation Format

Share Document