Genetic Neural Network Based Financial Distress Prediction

2014 ◽  
Vol 933 ◽  
pp. 921-925
Author(s):  
Xin Yun Liu ◽  
Heng Jun Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Financial Distress ◽  
Structural Parameters ◽  
Optimal Structure ◽  
Structure Parameters ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Genetic Neural Network

Enterprise financial distress prediction based on neural network has some disadvantages, such as complex structure, slow convergence rate and easily falling into local minimum points. The paper presents the genetic neural network based enterprise financial distress prediction. Firstly, the structural parameters of neural network model are encoded and connected into gene sequence to obtain an individual. A certain number of individuals make up a population. Secondly, after the reproduction, crossover and mutation operations upon the population, the best individual, that is the optimal structure parameters of neural network model, is obtained. Finally, the neural network model with the optimal structure parameters is trained by the training samples and the trained neural network model can realize enterprise financial distress prediction. The testing results show that the method achieves higher training speed and lower error rate.

scholarly journals Financial Distress Prediction Using GA-BP Neural Network Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1
Author(s):  
Lei Ruan ◽  
Heng Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Financial Distress ◽  
Test Model ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Bp Neural Network Model

Financial distress prediction, the crucial link of enterprise risk management, is also the core of enterprise financial distress theory. With currently global economic recession and the gradual perfection of artificial intelligence technology, the study in this paper begins by optimizing the back-propagation (BP) neural network model using the genetic algorithm (GA). In doing so, it can overcome the deficiency that the BP neural network model is slow in convergence and easily trapped into local optimal solution. The study then conducts training and tests on the optimized GA-BP neural network model, using financial distress data from Chinese listed enterprises. As can be seen from the experimental results, the optimized GA-BP neural network model is significantly improved in terms of the accuracy and stability in financial distress prediction. The study in this paper not only provides an effective test model for the automatic recognition and early warning of enterprise financial distress, but also contributes to new thoughts and approaches for the application of artificial intelligence in the field of financial accounting.

Parameter Optimization of High Deposition Rate Laser Cladding Based on the Response Surface Method and Genetic Neural Network Model

2021 ◽  
Vol 48 (6) ◽  
pp. 0602112
Author(s):  
庞祎帆 Pang Yifan ◽  
傅戈雁 Fu Geyan ◽  
王明雨 Wang Mingyu ◽  
龚燕琪 Gong Yanqi ◽  
余司琪 Yu Siqi ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Deposition Rate ◽  
Network Model ◽  
Parameter Optimization ◽  
Neural Network Model ◽  
High Deposition Rate ◽  
Surface Method ◽  
Genetic Neural Network

Modeling on Hydrocyclone Separation Performance by Neural Network

2011 ◽  
Vol 105-107 ◽  
pp. 185-188
Author(s):  
Feng Qin He ◽  
Ping Zhou ◽  
Jian Gang Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Input Parameter ◽  
Network Models ◽  
Separation Performance ◽  
Sampled Data ◽  
Neural Network Models

A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.

Neural Network Model for Predicting Anticancer Activity of Pyridopyrimidines Derivatives

2014 ◽  
Vol 905 ◽  
pp. 96-100 ◽  
Author(s):  
Xi Hhua Du ◽  
Wen Chang Zhuang
Keyword(s):  
Neural Network ◽  
Anticancer Activity ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Structural Parameters ◽  
Back Propagation ◽  
Molecular Structures ◽  
Molecular Connectivity ◽  
Molecular Connectivity Index

Molecular structures of pyridopyrimidines derivatives as known as dihydrofolate reductase (DHFR) inhibitors were investigated by using the neural network method. Based on the molecular connectivity, molecular connectivity index and molecular electronegativity distance vectors of 32 pyridopyrimidine derivatives were obtained. Among these parameters, three optimized structural parameters 1χ3χ and M17 as the input neurons of the artificial neural network were selected by step-wise regression. Then a 3:4:1 network architecture was employed and a satisfying neural network model for predicting anticancer activity (lg1/C) was constructed with the back-propagation (BP) algorithm. The total correlation coefficient R and the standard deviation S were 0.925 and 0.336 respectively that showed significantly nonlinear relationships between lg1/C and three structural parameters. It was concluded that the predictions of BP neural network are better than those of methods in the literatures.

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