Research on Optimization of Processing Parameter in Turning Process Based on BP Neural Network and Genetic Algorithm

2011 ◽  
Vol 480-481 ◽  
pp. 1358-1361
Author(s):  
Bao Dong Li ◽  
Xiao Hong Wu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Global Convergence ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Turning Process ◽  
Processing Parameter

A method of turning process parameter optimization combining neural networks with genetic algorithm was presented.Taking experimental data as samples,the model between processing parameter and processing function was established based on BP neural networks.Counter to various product objectives,processing parameter is optimized by genetic algorithm.When turning by the optimized process parameters, the error of the objective function <1%. It fully played their function which extensive mapping ability of neural networks and rapid global convergence of genetic algorithm.

Application of Hybrid Genetic Algorithm in Ground Stress Inversion

2011 ◽  
Vol 90-93 ◽  
pp. 337-341
Author(s):  
Ran Gang Yu ◽  
Yong Tian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Hybrid Genetic Algorithm ◽  
Inversion Method ◽  
Stress Inversion ◽  
Neural Network Inversion ◽  
Ground Stress

This paper propose genetic algorithm combined with neural networks, greatly improving the convergence rate of neural network aim at the disadvantage of the traditional BP neural network inversion method is easy to fall into local minimum and slow convergence.Finally, verified the feasibility and superiority of the above methods through the successful initial ground stress inversion of actual project.

New Evolutionary Neural Network Based on Continuous Ant Colony Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1773-1778
Author(s):  
Wei Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Evolutionary Programming ◽  
Ant Colony ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks ◽  
Xor Problem

The evolutionary neural network can be generated combining the evolutionary optimization algorithm and neural network. Based on analysis of shortcomings of previously proposed evolutionary neural networks, combining the continuous ant colony optimization proposed by author and BP neural network, a new evolutionary neural network whose architecture and connection weights evolve simultaneously is proposed. At last, through the typical XOR problem, the new evolutionary neural network is compared and analyzed with BP neural network and traditional evolutionary neural networks based on genetic algorithm and evolutionary programming. The computing results show that the precision and efficiency of the new neural network are all better.

Study on Construction Bidding Management Based on GA-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2423-2426
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Construction Project ◽  
Basic Theory ◽  
Slow Convergence ◽  
Artificial Neural

In the bidding evaluation, the deviations are likely to be brought about by experts' subjectivity, arbitrary and tendentiousness. A method for construction project bidding based on the BP neural network improved by GA (Genetic Algorithm) is proposed. On the basis of the basic theory of the BP neural network, discussions are provided on how to rectify the drawbacks of slow convergence and prone to convergence to minimum with the use of GA. The model is successfully applied GA - BP artificial neural networks to project, which are in concert with the result of experts. The study makes contribution to research about the evaluation system of construction bidding management.

Application of BP-GA Algorithm in Optimization of Process Parameters in Thin Strip Tandem Cold Rolling

2013 ◽  
Vol 774-776 ◽  
pp. 1042-1045
Author(s):  
Li Chen Wang ◽  
Ji Shun Song ◽  
Jian Zhang ◽  
Pan Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cold Rolling ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Rolling Force ◽  
Thin Strip ◽  
Tandem Cold Rolling ◽  
Optimization Of Process Parameters ◽  
Ga Algorithm

The process parameters of thin strip tandem cold rolling were optimized based on the BP neural network and the genetic algorithm with which the rolling energy consumption required was reduced and could contribute to the rolling force and the thickness control.

Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

2021 ◽  
Author(s):  
Lei Gao ◽  
Feng Li ◽  
Peng Da Huo ◽  
Chao Li ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Prediction Accuracy ◽  
High Performance ◽  
Orthogonal Experiment ◽  
Optimization Method ◽  
Factors Affecting

Abstract As a widely recognized optimization method, BP neural network can provide scientific guidance for the formulation of reasonable process parameters. However, due to the randomness of its own weights and thresholds, the prediction accuracy remains to be further improved. The forming and manufacturing of heterogeneous welded sheet is a new extrusion connection method. There are many factors affecting the bonding quality, which brings trouble to the evaluation of bonding strength and quality. In this paper, orthogonal experiment, finite element simulation and process experiment were used to design and verify the key process parameters that affected the bonding strength of heterogeneous sheets. BP neural network and genetic algorithm neural network were used to predict the bonding strength. The results showed that the genetic algorithm neural network model has higher reliability, and the prediction accuracy was 99.5 %. Compared with the traditional BP neural network, the prediction accuracy was improved by 5.78 %, and the error was reduced to 0.5 %. It has good generalization ability, and provides a new way for intelligent reliability evaluation of high performance heterogeneous sheets extrusion manufacturing.

Prediction of the Tension Softening Curve of Dam Concrete Based on BP Neural Network

2011 ◽  
Vol 311-313 ◽  
pp. 1935-1940
Author(s):  
Zhi Gang Zhao ◽  
Zhi Hua Yang ◽  
Zhi Fang Zhao ◽  
Min Min Zhu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Direct Tension ◽  
Softening Curve ◽  
Tension Tests ◽  
Tension Softening ◽  
Component Material ◽  
Dam Concrete

This paper predicted the tension softening curves of dam concrete by employing the BP neural networks based on the experimental data of direct tension tests of dam concrete. This approach can predict the tension softening curves for the same component material but different mixtures dam concrete without performing the direct tension tests which are complex and expensive.

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.

scholarly journals Effects of the Earthquake Nonstationary Characteristics on the Structural Dynamic Response: Base on the BP Neural Networks Modified by the Genetic Algorithm

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 69
Author(s):  
Yunlong Zhang ◽  
Dongsheng Du ◽  
Sheng Shi ◽  
Weiwei Li ◽  
Shuguang Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Dynamic Response ◽  
Earthquake Engineering ◽  
Bp Neural Network ◽  
Dynamic Responses ◽  
Training Data ◽  
Structural Dynamic ◽  
New Perspective

The intensity non-stationarity is one of the basic characteristics of ground motions, the influences of which on the dynamic responses of structures is a pressing issue in the field of earthquake engineering. The BP neural network modified by the genetic algorithm was adopted in this research to investigate the influence of intensity nonstationary inputs on the structural dynamic responses from a new perspective. Firstly, many training data were generated from the prediction formula of dynamic response. The BP neural network was then pre-trained by sparsely selected data to optimize the initial weights and biases. Finally, the BP neural network was trained by all data, and the mean square error of predicted responses compared with the target response were less than 10−5. The calculation formula of sensitivity was also derived here to quantify the influence of the input change on the output. The presented method combines the advantages of neural networks in nonlinear multi-variable fitting and provides a new perspective for the study of earthquake nonstationary characteristics and their influence on the structural dynamic responses.

Sign in / Sign up

Export Citation Format

Share Document