scholarly journals Automated fire risk evaluation of electrical installations in the man-machine system

2022 ◽  
Vol 1211 (1) ◽  
pp. 012017
Author(s):  
M A Gabova ◽  
O K Nikolsky ◽  
Yu D Shlionskaya
Keyword(s):  
Neural Network ◽  
Correlation Method ◽  
Principal Component ◽  
Fire Risk ◽  
Training Sample ◽  
Average Error ◽  
Industrial Complex ◽  
Principal Component Method ◽  
Fire Condition ◽  
Expert Assessments

Abstract The article considers approaches to the formation of a system of criteria for assessing the electrical installations fire condition of the agricultural and industrial complex. Based on the analysis of the literature, the conclusion is made about the appropriateness of the use of expert assessments. To implement the decision, a group of experts was assembled, on the basis of whose knowledge a list of 42 parametersζ characterizing the fire condition of the electrical installation was determined. To identify the relationships and form a method for calculating the estimated value of fire risk, experts assessed the fire condition of 70 electrical installations of the agricultural and industrial complex of the region. A knowledge base was formed from the resulting values. As a method of data analysis, it was decided to use neural networks, but the available sample is not sufficient for high-quality training of a neural network. Therefore, the correlation method and the principal component method were considered, and based on the calculations, it was decided to use a training sample consisting of 6 principal components for training a neural network. A neural network was trained on these data and the values of the average error were obtained sufficiently low, which may indicate sufficient accuracy of the generated model. The article also presents a conceptual scheme of a software package for automating calculations in accordance with the developed model.

Comparison on Dam Risk Consequences Comprehensive Evaluation Model

2013 ◽  
Vol 785-786 ◽  
pp. 1465-1468
Author(s):  
Wei Wei Sun ◽  
Lei Li ◽  
Hao Yao Zheng ◽  
Xue Ying Zhao
Keyword(s):  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Correlation Method ◽  
Principal Component ◽  
Principal Component Method ◽  
Weighted Sum Method ◽  
Comprehensive Evaluation Model ◽  
Gray Correlation ◽  
Mathematics Method ◽  
Dam Risk

Many evaluation factors are involved in dam risk consequences comprehensive evaluation, which exist plenty of uncertainty and correlation between each other. Combined the five methods, such as linear weighted sum method, fuzzy mathematics method, matter element method, gray correlation method and principal component method with dam risk consequences comprehensive evaluation, establishing five dam risk comprehensive evaluation models, applying to the level evaluation of Changlong Reservoir, Xialan Reservoir, Shibikeng Reservoir, Longshan Reservoir, Lingtan Reservoir successfully.

Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1939-1942
Author(s):  
Xing Mei Xu ◽  
Li Ying Cao ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Grain Yield ◽  
Bp Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Average Error ◽  
Main Factors

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

A Finger Vein Recognition Method Based on PCA-RBF Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 1653-1658 ◽  
Author(s):  
Cheng Bo Yu ◽  
Jun Tan ◽  
Lei Yu ◽  
Yin Li Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Training Sample ◽  
Finger Vein ◽  
Simpler Algorithm ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

scholarly journals Prediction of e-Learning Efficiency by Neural Networks

2012 ◽  
Vol 12 (2) ◽  
pp. 98-108 ◽  
Author(s):  
Petar Halachev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Balanced Scorecard ◽  
Small Sample Size ◽  
Principal Component ◽  
Real Data ◽  
Training Sample ◽  
Small Sample ◽  
The Neural Network ◽  
E Learning

Abstract A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

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