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.

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.

Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks

2012 ◽  
Vol 500 ◽  
pp. 198-203
Author(s):  
Chang Lin Xiao ◽  
Yan Chen ◽  
Lina Liu ◽  
Ling Tong ◽  
Ming Quan Jia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Soil Moisture ◽  
Training Data ◽  
Neural Network Training ◽  
Practical Test ◽  
Network Training

Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

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.

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.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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