Financial Crisis Warning Analysis for Companies Based on BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1529-1533
Author(s):  
Hong Min Zhang
Keyword(s):  
Neural Network ◽  
Financial Crisis ◽  
Capital Market ◽  
Bp Neural Network ◽  
Common Factor ◽  
Principal Component ◽  
Special Treatment ◽  
Good Prediction ◽  
Prediction Ability ◽  
Financial Status

This paper chose 11 financial criteria based on the principle for criteria selection. Due to the relevance of criteria, this paper collected 5 common factors with the method of principal component analysis of factor. The comprehensive score of every sample is got from the score of every common factor. Considering whether enterprises are special treated as auxiliary reference, 3 ranges of score are divided into 3 financial status accordingly: health, focus, crisis, disregarding the division criteria of special treatment or otherwise.Finally, the network got trained and tested by regarding 5 common factor as input and the financial status as output. It is found that BP neural network has a good prediction ability, high accuracy and application value, which will give a great assistance to the development of capital market in our country.BP: financial crisis warning, empirical analysis, factor analysis, BP neural network.

Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on BP Neural Network

2020 ◽  
Author(s):  
Hong-Wei He ◽  
Zao-Jian Zou
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Bp Neural Network ◽  
Mean Squared Error ◽  
Black Box ◽  
Good Prediction ◽  
Prediction Ability ◽  
System Identification Method ◽  
Identification Method ◽  
Maneuvering Motion

Abstract This paper deals with the black-box modeling of 3-DOF nonlinear maneuvering motion of surface ships by using system identification method based on BP neural network. A Mariner Class vessel is taken as the study object. The time series used in training and testing the network is the simulated data of a series of maneuvers, which is obtained by numerically solving the Abkowitz model using fourth-order Runge-Kutta method. A three-layer neural network is built to solve this multivariable regression fitting problem, and only one network model is trained to predict various ship maneuvers. Taking the mean squared error (MSE) as the loss function, the network’s weights are optimized by Levenberg-Marquardt (LM) algorithm effectively. The trained network is evaluated by several simulation tests, and it is shown that the network achieves good prediction ability and can predict the maneuvering motion as long as the control inputs and initial states of the ship motion are known.

scholarly journals Prediction of Manufacturing Quality of Holes Based on a BP Neural Network

2020 ◽  
Vol 10 (6) ◽  
pp. 2108
Author(s):  
Anyuan Jiao ◽  
Guofu Zhang ◽  
Binghong Liu ◽  
Weijun Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Hole Drilling ◽  
Al Alloy ◽  
Good Prediction ◽  
Drilling Process ◽  
Prediction Ability ◽  
Manufacturing Quality

In order to improve the manufacturing quality of holes (Φ3–Φ8 mm) and to optimize the hole drilling process in T300 carbon fiber-reinforced plastic (CFRP) and 7050-T7 Al alloy stacks, a prediction model of multiple objective parameter optimization was proposed based on a back propagation (BP) neural network algorithm. Four parameters of feed rate, spindle speed, drilling diameter, and cushion plate were taken as the input layer parameters to study the manufacturing quality of holes in four stack types: CFRP/Al, Al/CFRP, Al/CFRP/Al, and CFRP/Al/CFRP. Delamination and tearing defects often appear in the drilling process; thus, a certain degree of defects in CFRP was selected as the output parameter, in an effort to build a prediction model of drilling quality. After the neural network model of the optimized hole-making process of an 8–14–1 three-layer topology was corrected by 170 steps, the error was reduced to 0.00016882, the regression fitting was 0.99978, and the fitting error of training samples was 10−2~10−5. The prediction model of the number of defective holes provided basically similar results to the experimental data. This indicates that the prediction model based on a BP neural network has good prediction ability. Based on the prediction of parameters, verification tests were performed, and the number of defective holes in CFRP was reduced while the manufacturing quality of the holes was improved significantly; the qualified rate of manufactured holes reached 97%.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

Long-term runoff forecast using BP neural network based on climatic factors and mutual information method in the Qujiang Catchment of China

2020 ◽  
Author(s):  
Huihui Dai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Mutual Information ◽  
Bp Neural Network ◽  
Climatic Factors ◽  
Principal Component ◽  
Component Analysis ◽  
Information Method ◽  
Runoff Forecast

<p>The formation of runoff is extremely complicated, and it is not good enough to forecast the future runoff only by using the previous runoff or meteorological data. In order to improve the forecast precision of the medium and long-term runoff forecast model, a set of forecast factor group is selected from meteorological factors, such as rainfall, temperature, air pressure and the circulation factors released by the National Meteorological Center  using the method of mutual information and principal component analysis respectively. Results of the forecast in the Qujiang Catchment suggest the climatic factor-based BP neural network hydrological forecasting model has a better forecasting effect using the mutual information method than using the principal component analysis method.</p>

The selection of adaptive region of geomagnetic map based on PCA and GA-BP neural network

2020 ◽  
Author(s):  
Yang Chong ◽  
Dongqing Zhao ◽  
Guorui Xiao ◽  
Minzhi Xiang ◽  
Linyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Background Field ◽  
Characteristic Parameters ◽  
Positioning Accuracy ◽  
Matching Performance ◽  
Bp Neural Network Model ◽  
Selection Of

<p>The selection of adaptive region of geomagnetic map is an important factor that affects the positioning accuracy of geomagnetic navigation. An automatic recognition and classification method of adaptive region of geomagnetic background field based on Principal Component Analysis (PCA) and GA-BP neural network is proposed. Firstly, PCA is used to analyze the geomagnetic characteristic parameters, and the independent characteristic parameters containing principal components are selected. Then, the GA-BP neural network model is constructed, and the correspondence between the geomagnetic characteristic parameters and matching performance is established, so as to realize the recognition and classification of adaptive region. Finally, Simulation results show that the method is feasible and efficient, and the positioning accuracy of geomagnetic navigation is improved.</p>

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