Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

Parameters Optimization of the Heating Furnace Control Systems Based on BP Neural Network Improved by Genetic Algorithm

The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.

A Four-color Matching Method Combining Neural Networks with Genetic Algorithm

A brief review of color matching technology and its application of printing RGB images by CMY or CMYK ink jet printers is presented, followed by an explanation to the conventional approaches that are commonly used in color matching. Then, a four color matching method combining neural network with genetic algorithm is proposed. The initial weights and thresholds of the BP neural network for RGB to CMY color conversion are optimized by the new genetic algorithm based on evolutionarily stable strategy. The fourth component K is generated by using GCR (Gray Component Replacement) concept. Simulation experiments show that it is well behaved in both accuracy and generalization performance.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.

Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.

Predicting moisture condensation risk on the radiant cooling floor of an office using integration of a genetic algorithm-back-propagation neural network with sensitivity analysis

Pre-dehumidification time(τ_(pre))and pre-dehumidification energy consumption(E_(pre))play important roles in preventing the condensation of moisture on the floors of rooms that use a radiant floor cooling(RFC)system.However,there are few theoretical or experimental studies that focus on these two important quantities.In this study,an artificial neural network(ANN)was used to predict condensation risk for the integration of RFC systems with mixed ventilation(MV),stratum ventilation(SV),and displacement ventilation(DV)systems.A genetic algorithm-back-propagation(GA-BP)neural network model was established to predict τ_(pre) and E_(pre).Both training data and validation data were obtained from tests in a computational fluid dynamics(CFD)simulation.The results show that the established GA-BP model can predict τ_(pre) and E_(pre) well.The coefficient of determination(R^(2))of τ_(pre) and of E_(pre) were,respectively,0.973 and 0.956.For an RFC system integrated with an MV,SV,or DV system,the lowest values of τ_(pre) and E_(pre) were with the DV system,23.1 s and 0.237 kWh,respectively,for a 67.5 m^(3) room.Therefore,the best pre-dehumidification effect was with integration of the DV and RFC systems.This study showed that an ANN-based method can be used for predictive control for condensation prevention in RFC systems.It also provides a novel and effective method by which to assess the pre-dehumidification control of radiant floor surfaces.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

Optimization of a Lobed Mixer with BP Neural Network and Genetic Algorithm

A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and Genetic Algorithm:the ratio of lobe wavelength to height(η)and the rise angle(α)are selected as the design parameters,and the mixing efficiency,thrust and total pressure loss are the optimization objectives.The CFX commercial solver coupled with the SST turbulence model is employed to simulate the flow field of lobed mixer.A tetrahedral unstructured grid with 5.6 million cells can achieve the similar global results.Based on the response surface approximation model of the lobed mixer,it is necessary to avoid increasing or decreasingαandηat the same time.Instead,theαshould be reduced while theηis appropriately increased,which is conducive to achieving the goal of increasing thrust and reducing losses at the expense of a small decrease in the mixing efficiency.Compared with the normalized method,the non-normalized method with better global optimization accuracy is more suitable for solving the multi-objective optimization problem of the lobed mixer,and its optimal solution(α=8.54°,η=1.165)is the optimal solution of the lobed mixer optimization problem studied in this paper.Compared with the reference lobed mixer,theα,β(the fall angle)and H(lobe height)of the optimal solution are reduced by 0.14°,1.34°and 3.97 mm,respectively,and theηis increased by 0.074;its mixing efficiency is decreased by 4.46%,but the thrust is increased by 2.29%and the total pressure loss is decreased by 0.64%.Downstream of the optimized lobed mixer,the radial scale and peak vorticity of the streamwise voritices decrease with the decreasing lobe height,thereby reducing the mixing efficiency.For the optimized lobed mixer,its low mixing efficiency is the main factor for the decrease of the total pressure loss,but the improvement of the geometric curvature is also conducive to reducing its profile loss.Within the scope of this study,the lobed mixer has an optimal mixing efficiency(ε=74.14%)that maximizes its thrust without excessively increasing the mixing loss.

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.

Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

In recent years,the incidence of breast cancer is increasing and becomes one of the main causes of female death.The BP neural network optimized by standard genetic algorithm has slow convergence speed and is prone to local optimization,which makes the diagnosis accuracy of breast cancer decrease.This paper uses the improved genetic algorithm to optimize BP neural network by improving the selection operator of the standard genetic algorithm.The population diversity was first increased,and the probability of crossover and mutation was adaptively adjusted.Then deep optimization was executed on the initial weight threshold of BP network to speed up the network’s convergence,and the number of iterations was reduced.Finally breast cancer diagnose was performed.The experiment results show that both the fitness of the improved genetic algorithm and the recognition accuracy of breast cancer are improved.The shortcomings of the standard genetic algorithm optimized BP neural network algorithm in breast cancer diagnosis are well solved.

Optimization of turbine cold-end system based on BP neural network and genetic algorithm

因为它是 indispensible，一台蒸气汽轮机的冷结束的系统的操作条件在单位的经济和安全上有直接影响热力量单位的辅助系统。许多因素影响一台蒸气汽轮机的冷结束的操作；因此，操作模式需要被优化。联合起来的 1000 MW ultra-supercritical (USC ) 的优化分析，汽轮机冷结束的系统，被执行利用背繁殖(BP ) 有基因算法(GA ) 的神经网络方法优化分析。在不同条件下面的优化 condenser 压力被获得，并且结果优化参数具有到性能和系统的经济操作的意义。

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.

Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum

In this study, a hybrid algorithm combining genetic algorithm （GA） with back propagation （BP） neural network （GA-BP） was proposed for extracting the characteristics of multi-peak Brillouin scattering spectrum. Simulations and experimental results show that the GA-BP hybrid algorithm can accurately identify the position and amount of peaks in multi-peak Brillouin scattering spectrum. Moreover, the proposed algorithm obtains a fitting degree of 0.9923 and a mean square error of 0.0094. Therefore, the GA-BP hybrid algorithm possesses a good fitting precision and is suitable for extracting the characteristics of multi-peak Brillouin scattering spectrum.

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

The leaf area index(LAI)is an important agroecological physiological parameter affecting vegetation growth.To apply the genetic algorithms neural network model(GANNM)to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar(GF-3 SAR)images and GaoFen-1 Wide Field of View(GF-1 WFV)images,the Xiangfu District in the east of Kaifeng City,Henan Province,was selected as the testing region.Winter wheat LAI data from five growth stages were combined,and optical and microwave polarization decomposition vegetation index models were used.The backscattering coefficient was extracted by modified water cloud model(MWCM),and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI.The root mean square error(RMSE)and determination coefficient(R2)were used as evaluation indicators of the model.The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model;the R2 was higher than 0.8,and RMSE was lower than 0.3,indicating that the model could accurately invert the growth status of winter wheat in five growth stages.

CNC Thermal Compensation Based on Mind Evolutionary Algorithm Optimized BP Neural Network

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.