Ability Of Back-propagation Neural Network Research Articles

Prediction analysis of cotton yield based on BP neural network and multiple linear regression

With the rapid development of the economy and agricultural production, cotton is an indispensable crop in people's production and life, and the average annual cotton yield is particularly significant in production. The unit yield of cotton is affected by many factors, so it is extremely important to analyze the factors affecting future cotton yield forecast. This article varies seven relevant indicators, including fertilizer cost,consumption of agricultural plastic, pesticide cost, seed cost, irrigation cost, chlorpyrifos control area, and labor input, and establishes a prediction model based on Back Propagation (BP) neural network. The model uses the function approximation ability of BP neural network and gradient descent algorithm to predict the nonlinear functional relationship between the unit output and the seven factors. Meanwhile, a multiple linear regression model is used to forecast the unit yield and the prediction accuracy of the two methods for unit output is compared. The experimental results show that compared with multiple linear regression, BP neural network has higher accuracy in predicting cotton unit yield, which can be used as an idea for future prediction.

Prediction of fatigue crack growth under variable amplitude loading by artificial neural network-based Lagrange interpolation

Artificial neural network has been several applications on fatigue crack, with prediction on fatigue crack growth life often serving as milestones. A typical difficulty in predicting the life curve of fatigue crack growth is the retardation of crack growth caused by the overload effect. Overload retardation, resulting in the lack of experiment data points in the retardation interval, is a long-term challenge for predicting fatigue crack growth. We improve the ability of back-propagation neural network to predict fatigue crack growth life by Lagrange interpolation, an interpolation for local data points. It combines the trend of whole data point to deal with the retardation interval, and interpolates multiple data points in the retardation interval when there is a lack of local data, and uses a neural network to construct the prediction formula of crack growth through self-optimization. In the study involving constant and variable amplitude fatigue crack growth experiments, the proposed method is proved to improve, with statistical significance, the predictive ability on the whole range of experiment data. The method is simple and accurate. Consequently, it is helpful to solve the problem of fatigue crack growth life of engineering structure.

Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy

In recent years, ultra-fine-grained Fe–C alloy have received widespread attention because of their high levels of strength, hardness, and wear resistance. However, these alloy have strict requirements on control of the process parameters during production to meet product specifications. This study was aimed at designing a prediction model for their mechanical properties, which could ultimately promote the industrial development of ultra-fine-grained Fe–C alloy. The effect of process parameters on the mechanical properties of ultra-fine-grained Fe–C alloy was analyzed by electron backscatter diffraction (EBSD) and scanning electron microscope (SEM). By fusing experimental combinations of alloy composition, rolling process, annealing process, and resultant mechanical properties into a dataset for training and verification, a machine-learning-based prediction model of mechanical properties of ultra-fine-grained Fe–C alloy was constructed and tested. When choosing the machine learning algorithm, the best network model was obtained for this application by optimizing a conventional backpropagation (BP) network structure: a three-layer network structure, with a learning rate of 0.5, 18 hidden layer neurons, and activation functions of the hidden and output layers, namely Tansig and Purelin (MATLAB functions), respectively. Finally, the sample data was divided into training set and verification set, and the prediction accuracy, efficiency and generalization ability of BP neural network (BPNN), genetic neural network (GA-BP) and support vector machine (SVM) were compared and analyzed. The superiority of BPNN in the prediction of mechanical properties of ultrafine grained Fe–C alloy was determined, and the mean absolute percentage error (MAPE) of the BPNN for tensile strength, yield strength and uniform elongation are 3.02%, 4.47% and 6.47% respectively, and the number of training steps is 412, which are less than the other two models. The result lays a foundation for the production of high stability and high performance steel products.

Calculation of golf course environmental cost based on BP neural network

Golf is an elegant outdoor sport, but it is not popular with the Chinese people, largely because the construction of golf courses will cause a series of environmental problems. In this paper, the Cost-significant(CS) is introduced to calculate the environmental cost of golf course construction. According to the data of 14 golf course constructions consulted and sorted, the Cost-significant Items(CSIs) in golf course construction project is identified, and innovatively integrate data into the index of the project. Using the strong nonlinear mapping ability of Back-Propagation Neural Network(BPNN) Algorithm, a general environmental cost estimation model for golf course construction is trained. The comparison between the model fitting results and the original data shows that the model not only has high fitting accuracy, but also avoids involving complex data operation and complicated processing steps, which greatly simplifies the calculation process.

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.

Modeling the Removal of Phenol Dyes Using a Photocatalytic Reactor with SnO2/Fe3O4 Nanoparticles by Intelligent System

The objective of this study was to model the extent of improvement in the degradability of phenol dyes by SnO2/Fe3O4 nanoparticles using a photocatalytic reactor. The effect of operative parameters including catalyst concentration, initial dye concentration, stirring intensity, and UV radiation intensity on the photocatalytic batch reactor during removal of phenol red was investigated. Fractional factorial design and response surface methodology were used to design the experiment layout. The SnO2/Fe3O4 nanoparticles were synthesized using the core-shell method. The results of x-ray diffraction and transmission electron microscopy showed the successful synthesis of these nanoparticles. The ability of back-propagation neural network (BPNN), radial basis function neural network (RBFNN), and adaptive-network-based fuzzy inference system (ANFIS) in predicting the performance of photocatalytic reactor was also investigated. It was found that BPNN has better ability in predicting dye removal (%) than RBFNN, ANFIS, and the regression model. The best architecture of BPNN was a network consisting of three hidden layers with 20-30-20 neurons. The BPNN predicted the output values with a high determination coefficient (R 2) value of 0.0.9718, while the predicted R 2 of the regression model was 0.8978, and the predicted R 2 values of RBFNN and ANFIS were 0.8011 and 0.9023, respectively.

Enhancement predicting accuracy for elastin-like polypeptides temperature transition by back propagation neural network.

Elastin-like polypeptides (ELPs) have been widely used to promote the development of a variety of smart biomaterials. Transition temperature is a key attribute of ELPs central to ELPs researches. Therefore, it is essential to establish predictive models of transition temperature that are both computationally efficient and reliable by employing simple parameters. Back propagation neural network (BPNN), a powerful feed-forward algorithm designed to solve problems with overwhelming complexity, has been successfully used in non-linear predictor model. In this study, two pH-sensitive ELPs were expressed, purified and determined for temperature transition across a range of pH. The pH, concentration and molecular weight (MW) as well as isoelectric point (PI) and pseudo amino acid (PseAA) of these two ELPs were adopted as input parameters. Support vector machine (SVM) and back propagation neural network (BPNN) were performed respectively. We selected Lamda (λ) value by training set and evaluated predictor model by jackknife test that combined with Uniform Design (UD). According to the results of BPNN and SVM, whose mean absolute error (MAE) of BPNN model jackknife test were 4.80 and 4.95 respectively, the predictive ability of BPNN is a minor improvement over SVM. Applying Mackay's data, MAE of BPNN jackknife test was 2.02, while the MAE between experimental and predicted transition temperature was 2.30 in Mackay's predictor model. Compared with Mackay predictor model, the enhancement in the accuracy indicates that the proposed BPNN method could play a complementary role to predict ELPs transition temperature.

Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation

With global non-renewable resources and environmental issues becoming more apparent, the development of new energy vehicles have become the trend of auto industry. Hybrid vehicle becomes the key development of new energy vehicles with its long distance, low pollution, low fuel consumption characteristics and so on. The battery performances directly influence the quality of the whole vehicle performance. Considering the importance of the battery state of charge (SOC) estimation and the nonlinear relationship between the battery SOC and the external characteristic, genetic algorithm (GA) and back propagation (BP) neural network are proposed. Because of the strong global search capability of the genetic algorithm and the generalization ability of BP neural network, the hybrid vehicle Ni-MH power battery GA-BP charging model is designed. In this approach, the network training speed is superior to the traditional BP network. According to the real-time data of the batteries, the optimal solution can be concluded in a short time and with high estimation precision.

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(R) 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.

Classification of smoking cessation status with a backpropagation neural network

This study examined the ability of a backpropagation neural network (BPNN) classifier to distinguish between current and former smokers in the 2000 National Health Interview Survey (NHIS) sample adult file. The BPNN classifier performance exceeded that of random chance, with asymmetric 95% confidence intervals for A z (area under receiver operating characteristic curve) = (0.7532, 0.7790). Separation of current and former smokers was imperfect, as illustrated by the receiver operating characteristic (ROC) curve. Additionally, performance did not exceed that of a comparison classifier created using logistic regression. Attribute subset selection identified three novel attributes related to smoking cessation status. This study establishes the ability of backpropagation neural networks to classify a complex health behavior, smoking cessation. It also illustrates the hypothesis-generating capacity of data mining methods when applied to large population-based health survey data. Ultimately, BPNN classifiers of smoking cessation status may be useful in decision support systems for smoking cessation interventions.

Application of Genetic Neural Networks to Real-Time Intersection Accident Detection Using Acoustic Signals

A genetic neural network (GNN) classification method using acoustic signals is proposed for real-time accident detection at intersections. Back-propagation neural networks (BPNNs) have been widely used in pattern classification. They have fast computation speeds that are desirable for real-time detection systems. However, they tend to converge to local optimums, which consequently affects classification accuracy. The proposed GNN uses a genetic algorithm to improve the global searching ability of BPNNs. GNN performance (i.e., detection rate, false alarm rate, and detection time) is compared with that of a widely used probabilistic neural network (PNN). Test results indicate that the GNN performs comparably to a PNN but with much less computation time. Results of the GNN transferability analysis indicate that the GNN method also is robust and can be successfully applied to intersection accident detection with training data from different sources. Computationally inexpensive and highly accurate, the new GNN...