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 Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

An Optimizing Design Approach for the Fiber Manufacturing Based on the Immune Genetic Algorithm-Optimized Neural Network

2011 ◽  
Vol 267 ◽  
pp. 19-24
Author(s):  
Hui Zhong Zhu ◽  
Yong Sheng Ding ◽  
Xiao Liang ◽  
Kuang Rong Hao ◽  
Hua Ping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Immune Genetic Algorithm ◽  
System A ◽  
The Neural Network ◽  
Proposed Model ◽  
Optimizing Design ◽  
Spinning Process ◽  
Fiber Manufacturing ◽  
Network Component

A novel neural network-based approach with immune genetic algorithm is proposed to conduct the optimizing design for the industrial filament manufacturing system. A new model is proposed in this paper to acquire better filament quality during such process. The proposed model was a combination of two components, namely, a traditional neural network which is used to simulate and an immune genetic algorithm-based part which is to improve the performance of the neural network component. Simulation results demonstrate that the proposed method can efficiently demonstrate the spinning process of filament and conduct the prediction of the filament quality with the production parameters as input data. Meanwhile, the proposed method enjoys faster speed and more precise accuracy, compared with traditional methods.

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 4400-4404 ◽  
Author(s):  
Chun Mei Zhu ◽  
Chang Peng Yan ◽  
Xiao Li Xu ◽  
Guo Xin Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Mixed Model ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Speed Up ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 79-85 ◽  
Author(s):  
Feng Yu ◽  
Zhi Qing Wang ◽  
Xiao Zhong Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Learning Ability ◽  
Time Frequency ◽  
Proposed Model ◽  
Real Coded Genetic Algorithm ◽  
Self Learning ◽  
Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

A Neural Network Approach for Milling Process Control

2004 ◽  
Vol 471-472 ◽  
pp. 107-111 ◽  
Author(s):  
Z. Yang ◽  
T. Huang ◽  
Y.M. Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fuzzy Logic Control ◽  
Milling Process ◽  
Gradual Change ◽  
Neural Network Approach ◽  
Logic Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

One of key approaches to improve the productivity is to control with constant force in the milling process by adjusting the feed rate. In order to overcome the mismatch model occurred in adaptive control and inaccurate deducing regulation in fuzzy logic control, a three-layer BP neural network is designed for tracing reference force. First of all, control arithmetic is given, and a series of simulation work is achieved to determine the study factor. At last, aimed at two working conditions with abrupt and gradual change of cutting depth, the correctness and effectiveness of the neural network controller are proved by experiments.

Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 2598-2602 ◽  
Author(s):  
Yan Cong Li ◽  
Lian Hong Zhang ◽  
Chun Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Hydraulic Press ◽  
Important Indicator ◽  
Network Training ◽  
The Neural Network ◽  
Forecast Result

Workpiece’s precision is an important indicator of hydraulic press. In order to accurately predict the accuracy of the part, a method that combined the genetic algorithm and neural network is put out. Design of orthogonal experiment (DOE) is used to determine the input samples of neural network training and testing samples. The output samples are obtained by finite element analysed method (FEA). Through optimizing weights and thresholds of BP neural network using genetic algorithms, prediction model of workpiece’s precision is established. The established predict model overcomes the shortcomings of slow to convergence and easy to fall into the local minimum point of BP neural network model . By comparing the neural network forecast result with FEA ‘s results, it can be seen that the established prediction model has good fitting and generalization ability. So the model can be used to predict the workpiece’s precision.

Application and Study of BP Neural Network and Genetic Algorithm for Optimizational Parameters Based on MATLAB

2013 ◽  
Vol 325-326 ◽  
pp. 1726-1729 ◽  
Author(s):  
Yu Hua Zhu ◽  
Dian Zheng Zhuang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Nitric Acid ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
The Neural Network ◽  
Neural Network Toolbox

BP neural network modeling is introduced using MATLAB neural network toolbox function, In order to find the non-linear mathematical model between data. And process parameters is optimized combination the neural network and genetic algorithm, The method has been applied to optimize parameters for nitric acid device, and proved to be highly importance, Programming with MATLAB is very brief and practicable to optimize parameters using neural network and genetic algorithm.

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