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

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.

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The study of automatic insertion and deletion of fuzzy rules in fuzzy neural network models

Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium ◽

10.1109/afss.1996.583599 ◽

2002 ◽

Author(s):

Jyh-Ming Chen ◽

Shuan-Hao Wu ◽

Hahn-Ming Lee

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Network Models ◽

Fuzzy Rules ◽

Neural Network Models ◽

Insertion And Deletion ◽

Fuzzy Neural

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

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9117 ◽

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.230-232.1104 ◽

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.

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