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.

Research on Risk Assessment of Farmers’ Microcredit Based on BP Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 1068-1072
Author(s):  
Wei Ji ◽  
Xue Fang Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Index System ◽  
Practical Application ◽  
Assessment Index ◽  
Rural Credit ◽  
Assessment Index System ◽  
Essential Support

Our government always pushes hard forward the farmers’ microcredit of the rural credit cooperation. But actually, its risk is higher than other kinds of loan. Based on analyzing the risk of farmers’ microcredit, this paper builds risk assessment index system of farmers’ microcredit by using BP neural network. Through the paper, it can provide essential support and practical application for relative departments of farmers’ microcredit.

Safety Risk Assessment of Highway Special Maintenance Project Based on BP Neural Network

2011 ◽  
Vol 368-373 ◽  
pp. 3175-3179
Author(s):  
Wei Tian ◽  
Hui Min Li ◽  
Rui Qi Yan ◽  
Yun Xiang Hu
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Safety Management ◽  
Construction Safety ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Safety Risk ◽  
Assessment Index ◽  
Assessment Index System

16 items of assessment indexes are selected to build up the safety risk assessment index system of special maintenance project according to the construction safety characteristics of highway special maintenance project, based on which, the safety risk assessment model of special maintenance project based on BP neural network is brought forward. The assessment model has been trained, checked and analyzed through example and it turns out that the effective safety risk assessment of highway special maintenance project can be calculated based on this model, which also supplies decision-making basis for the project safety management.

Research on Safety Comprehensive Assessment Method for Air Traffic Control Based on RBF Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 2671-2674
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li
Keyword(s):  
Neural Network ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Safety Assessment ◽  
Rbf Neural Network ◽  
Assessment Method ◽  
Air Traffic ◽  
Comprehensive Assessment ◽  
Assessment Index ◽  
Assessment Index System

To assess the safety situation in air traffic control effectively, a comprehensive assessment method based on RBF neural network is proposed. At first, the safety assessment principle based on RBF neural network is introduced, and then the safety assessment index system for air traffic control is established from four aspects of human, machine, environment and management. Simulation example of ATC safety assessment gives us a satisfactory result for RBF neural network.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2722-2725
Author(s):  
Jian Xue Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hybrid Algorithm ◽  
Chemical Process ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Continuous Stirred Tank Reactor ◽  
The Neural Network ◽  
Continuous Stirred Tank

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Sign in / Sign up

Export Citation Format

Share Document