Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

CATEGORIZATION AND IDENTIFICATION OF HUMAN FACE IMAGES BY NEURAL NETWORKS: A REVIEW OF THE LINEAR AUTOASSOCIATIVE AND PRINCIPAL COMPONENT APPROACHES

1994 ◽  
Vol 02 (03) ◽  
pp. 413-429 ◽  
Author(s):  
D. VALENTIN ◽  
H. ABDI ◽  
A.J. O’TOOLE
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Processing ◽  
Linear Models ◽  
Network Models ◽  
Principal Component ◽  
Human Face ◽  
Neural Network Models ◽  
Face Images ◽  
Face Categorization

Recent statistical/neural network models of face processing suggest that faces can be efficiently represented in terms of the eigendecomposition of a matrix storing pixel-based descriptions of a set of face images. The studies presented here support the idea that the information useful for solving seemingly complex tasks such as face categorization or identification can be described using simple linear models (linear autoassociator or principal component analysis) in conjunction with a pixel-based coding of the faces.

scholarly journals Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1976
Author(s):  
Leilei Zou ◽  
Jiangshan Zhang ◽  
Yanshen Han ◽  
Fanzheng Zeng ◽  
Quanhui Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Continuous Casting ◽  
Deep Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Ability ◽  
Internal Crack ◽  
Strong Nonlinearity ◽  
Neural Network Models

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

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