Optimal control of SMA cables system based on genetic optimized BP neural network constitutive model

2017 ◽  
Vol 90 (1) ◽  
pp. 44-57
Author(s):  
Yuan Zhou ◽  
Deli Wang ◽  
Sheliang Wang ◽  
Juan Wang
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Constitutive Model ◽  
Bp Neural Network

Constitutive Model Identification of Rock Based on Artificial Intelligence

2011 ◽  
Vol 368-373 ◽  
pp. 2509-2516 ◽  
Author(s):  
Sheng Jun Sha ◽  
Xiao Hao ◽  
Shu Hua Zhai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Genetic Programming ◽  
Constitutive Model ◽  
Bp Neural Network ◽  
Model Identification ◽  
Terminal Set ◽  
Searching Ability ◽  
Intelligent Methods ◽  
Optimum Solution

The Identification of rock constitutive model is a typical nonlinear system problem; introduction of the intelligent methods has greatly stimulated the research. In this paper, genetic algorithm , BP neural network and genetic programming are used in the identification of constitutive model ,the capacity of three intelligent methods in the model identification is compared ,whose results show that genetic algorithm can largely reduce the probability of trapping into localized optimum solution by means of its global searching ability ,however , because its pre-assumption of constitutive model has already lead to some error ,its fitting results are not so good . BP neural network and genetic programming need not pre-assume the structure of constitutive model ,the nonlinear mapping expressions between stress and strain are obtained by self-organization and self-learning .But ,the determination of BP neural network structure is experience-depending and time-consuming . In addition, genetic programming need not define its structure, so long as the functional set and terminal set are chosen, after series of genetic operations, the nonlinear relations between variables are outputted, therefore, the operation of genetic programming is simple which realize the visualization of the constitutive equations.

scholarly journals Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Hui Li ◽  
Yongsui Wen ◽  
Wenjie Sun
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Dynamic Programming ◽  
Complex Systems ◽  
Bp Neural Network ◽  
Optimal Control Problems ◽  
Dynamic Programming Algorithm ◽  
Data Modeling ◽  
Control Problems ◽  
Heuristic Dynamic Programming

When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP) technique, which is based on the BP neural network algorithm (BP-DHP), has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM) algorithm (ELM-DHP) was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.

scholarly journals Construction of Aluminum Alloy Constitutive Model Based on BP Neural Network and the Study of Non-isothermal Hydroforming

2021 ◽  
Author(s):  
Xiao Jing Liu ◽  
Xue Feng Ma ◽  
Chao Li ◽  
Jin Qin ◽  
Peng Chen
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Aluminum Alloy ◽  
Constitutive Model ◽  
High Precision ◽  
Bp Neural Network ◽  
Uniaxial Tensile ◽  
High Quality ◽  
Spherical Bottom ◽  
Cylindrical Cup

Abstract With the continuous development of high-end technology in aerospace and automotive, in order to meet the needs of high performance, high precision and lightweight of parts, the materials used are lightweight and strong, but very difficult to deform, so it is difficult to obtain high-quality, high-precision parts. In order to improve the forming quality and precision of parts, taking 6061-T6 aluminum alloy cylindrical cup with spherical bottom as the research object, the non-isothermal hydroforming process is studied by combining numerical simulation with experiment. The key of numerical simulation technology lies in the accuracy of simulation, which depends on the establishment of a suitable rheological stress relationship. So, a constitutive model that can truly reflect the thermoforming characteristics of 6061-T6 aluminum alloy materials is established through a uniaxial tensile test and BP neural network. Applying the constitutive model to the study of numerical simulation of non-isothermal hydroforming, the cylindrical cup with spherical bottom with high quality is obtained through the optimization of non-isothermal process parameters. After experimental verification, the results of numerical simulation are highly compatible with the actual forming results of parts, and have high reliability.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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