scholarly journals Research on BP neural network in enterprise credit rating management based on artificial intelligence era

2021 ◽  
Vol 1848 (1) ◽  
pp. 012165
Author(s):  
Jingyi Ye ◽  
Xiao Han
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Credit Rating ◽  
Enterprise Credit

scholarly journals Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hua Peng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Credit Rating ◽  
Hybrid Technique ◽  
Neural Network Approach ◽  
Enterprise Credit ◽  
The Neural Network ◽  
Proposed Model ◽  
Rating Model

In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

scholarly journals Enterprise Credit Rating Based on Genetic Neural Network

2018 ◽  
Vol 227 ◽  
pp. 02011
Author(s):  
Yulin Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Credit Rating ◽  
Bp Algorithm ◽  
Slow Convergence ◽  
Enterprise Credit ◽  
The Neural Network ◽  
Network Method ◽  
Genetic Neural Network

The genetic BP algorithm is used to modify and optimize the connection weights and thresholds of the neural network, which solves the problem that BP neural network has slow convergence speed and may fall into local minimum to a certain extent. The accuracy of rating indicates that the genetic neural network method is very suitable for enterprise credit rating.

scholarly journals Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method

2019 ◽  
Vol 9 (6) ◽  
pp. 1039 ◽  
Author(s):  
Guohua Liu ◽  
Jian Zheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Compressive Strength ◽  
Bp Neural Network ◽  
Strength Development ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Compressive Strength Development ◽  
Compressive Strength Of Concrete ◽  
Conventional Models

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

The Application of Artificial Intelligence Technology in the Selective Laster Sintering

2014 ◽  
Vol 1037 ◽  
pp. 236-239
Author(s):  
Li Yuan Cai ◽  
Qing Shun Wang ◽  
Wei Sun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Research Method ◽  
Control Method ◽  
Laser Sintering ◽  
Sintering Process ◽  
Optimal Parameters ◽  
Artificial Intelligence Technology ◽  
Neural Network Algorithm

Based on laser sintering constituency as the research object, this paper aimed at the perspective of artificial intelligence technology. It uses the new control theory and research method of BP neural network algorithm and tries to provide reference for optimizing the sintering process of laser district. This paper argues that the application of artificial intelligence technology to laser sintering constituency. Through the simulation, it can make up for the inadequacy of the traditional control method. Under certain conditions, the goal of process optimization will be achieved by finding the optimal parameters.

Study on the Influence IDs Model of the Structure and the Behavior Choice

2013 ◽  
Vol 380-384 ◽  
pp. 1354-1357
Author(s):  
Ya Ni Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Utility Function ◽  
Bp Neural Network ◽  
Experimental Result ◽  
Individual Utility ◽  
Decision Making Problem ◽  
Individual Utility Function ◽  
Behavior Choice

The complicated decision making problem is one of the important components for the study on the system of artificial intelligence area. This thesis, based on the Bayesian technology and decision-making theory, is going to optimize the traditional IDs model and improve the ability of expression of the model. and also by using the sum of individual utility function instead of the joint utility function to create the BP neural network to study the utility function structure of the IDs. The experimental result shows the method mentioned above is effective.

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.

The effective model of transformation of ideological and political education in universities based on artificial intelligence

2021 ◽  
pp. 1-9
Author(s):  
Yu Zhang ◽  
Xuying Sun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Target Prediction ◽  
Tracking Algorithm ◽  
Ideological And Political Education ◽  
Political Education ◽  
Tracking Accuracy ◽  
Transmission Of Knowledge ◽  
Tracking Model

In the context of artificial intelligence, the path of knowledge transmission needs to be transformed. In essence, the transmission of knowledge and the transformation of information transmission methods are integrated. This paper studies the foreign object tracking algorithm, analyzes the error in the target tracking algorithm, and uses the BP neural network principle to modify the IMM algorithm. Aiming at the problem of low tracking accuracy when the target is maneuvering, this paper analyzes the linearization error of Kalman filter and builds a BP neural network to correct the tracking model of IMM. The model creates a target prediction training set and a test set, optimizes the parameters of the neural network, and conducts simulation experiments using MATLAB, which proved that the model had a higher accuracy in predicting the target trajectory of foreign objects. Therefore, the transformation of ideological and political teaching mode in colleges and universities can be realized, and the intelligent classroom of ideological and political education and intelligent communication have technical support.

Development of Slab Caster Break-Out Alarm ANN System

2011 ◽  
Vol 415-417 ◽  
pp. 321-324
Author(s):  
Yan Jin ◽  
Hui Ding ◽  
Zhi Bing Tian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Expert System ◽  
Continuous Casting ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Slab Caster ◽  
Slab Continuous Casting ◽  
Prediction Module ◽  
Decision Module

In this paper the Slab Caster Break-out Alarm ANN System is introduced, which has been developed with artificial intelligence and logic technique, knowledge of continuous casting expert system. The characteristics of the Slab Caster Break-out Alarm ANN System is that the breakout predict of the system is achieved with the logic breakout prediction module and BP neural network breakout module; The predicting results of the two methods are inputted into the expert system decision module, this module can provide the last predicting results. This would increase the prediction accuracy, and improve the process of slab continuous casting.

scholarly journals Backpropagation Neural Network Artificial Intelligence Algorithm-Based Magnetic Resonance Imaging Image Feature Analysis in the General Anesthesia Hip Arthroplasty

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yufang Li ◽  
Xin Wang ◽  
Qian Zhao ◽  
Xiaoqing Zhang ◽  
Manyun Bai
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Hip Arthroplasty ◽  
Bp Neural Network ◽  
Resonance Imaging ◽  
Backpropagation Neural Network ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

Objective. This study aimed to present an investigation of the clinical significance of magnetic resonance imaging (MRI) images obtained based on the backpropagation neural network (BPNN) artificial intelligence algorithm for hip arthroplasty under general anesthesia. Methods. In this study, a case-review method was used to collect 100 patients requiring total hip replacement. They were then randomly divided into an observation group and a control group. Based on the neural network algorithm, the images of the two groups of patients were analyzed to judge their accuracy. Then the sensitivity, specificity, and accuracy of MRI images based on neural algorithms were compared with those processed by radiologists. Results. It was found that MRI processed by BP neural network had good accuracy in the diagnosis of hip joint diseases compared with CT. Meanwhile, the images processed by BP neural network had good specificity and accuracy compared with the images processed by radiologists. Conclusion. Imaging images obtained by BPNN artificial intelligence algorithm were more accurate than CT images, which had more guiding value for surgeons in operation.

Sign in / Sign up

Export Citation Format

Share Document