Prediction of Ore Quantity Based on GA-BP Neural Network

A genetic algorithm is used to optimize the weights and thresholds of the BP neural network in this paper, this method improves the disadvantages of the BP neural network. For example, It is insensitive to weights and thresholds. In addition, it has a slow convergence rate and is prone to fall into local minima. etc. To verify the improvement effect, the BP neural networks and the improved BP neural networks were used to simulate the weather in Rizhao city, Shandong Province, respectively. The results of simulation show that the GA-BP neural network model is superior to BP neural network model in the accuracy of temperature prediction in long-term weather. Next, the GA-BP neural network was further optimized by selecting the optimal number of hidden layers and changing the impact factors, and the prediction results were fitted. The analysis shows that the accuracy of the improved GA-BP model in prediction is higher.

In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

The anisotropy and nonuniformity of wood-plastic composites (WPCs) affect the milling tool, which rapidly wears during high-speed milling of WPCs. Thus, the evolution mechanism of tool failure becomes complicated, and the prediction of tool wear cannot be precisely described mathematically. A neural network based on tool wear test was proposed to predict the tool wear condition during high-speed milling of WPCs. The traditional backpropagation (BP) neural network easily falls into the local optimal solution. A genetic algorithm (GA-BP) neural network prediction model was established by using the GA to optimise its initial weight and threshold. The BP model and the GA-BP model were evaluated in terms of mean square error and training times, and the generalisation verification was applied to the prediction model. After analysing and comparing the results of the two models, the GA-BP neural network model has better training speed and accuracy under the test conditions. The relative error between the predicted value and the actual value is controlled within 5%.

With the Belt and Road strategy deeply rooted in people's hearts, it is an inevitable choice for China and countries (or regions) along the Belt and Road strategy to build a science and technology innovation community. In this study, the high-tech industries of major provinces and cities in China are taken as the research object, and the collaborative innovation process and mechanism of each innovation subject are taken as the research purpose. By introducing the collaborative innovation concept of the innovation community, we construct the innovation performance evaluation index system to optimize the model of the innovation community performance evaluation via using neural networks. The neural network is one of the most widely used algorithms among deep learning algorithms, and this paper mainly analyzes the characteristics of Back Propagation (BP) neural network in the performance evaluation, which has strong local optimization but slow convergence speed. We build a BP neural network based on the genetic algorithm, and this neural network makes the performance evaluation model has the characteristics of strong global optimization and fast convergence speed. However, at the same time, it also has the risk of early convergence and lacks the ability of the global search. On this basis, we propose an improved genetic BP neural network based on simulated annealing in this paper. The improved simulated annealing algorithm not only can reduce error but also has a stronger local convergence and a faster evolution speed, which can obtain a satisfying global optimal solution. When we apply the improved simulated annealing genetic BP neural network in the performance evaluation of the science and technology innovation community, the simulation results show that this algorithm can be a reliable performance evaluation method and easier to obtain the optimal solution than other algorithms. Besides, the feasibility of the evaluation system and method is verified.

Icing condition prediction of wind turbine blade by using artificial neural network based on modal frequency

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

Internal leakage rate prediction and failure diagnosis of buried pipeline ball valve based on valve cavity pressure detection

ABSTRACTGlobal Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a new remote-sensing technique, and it can be used to estimate near-surface soil moisture from Signal-to-Noise Ratio (SNR) data. Considering the effects of vegetation changes on GNSS-IR in some environments, a non-linear inversion method for soil moisture is proposed. Firstly, the SNR data and satellite elevation angles are solved using Translation, Editing, and Quality Checking. The direct and reflected signals are separated using a low-order polynomial; then, a sinusoidal fitting model of the reflection signal is established; it is used to obtain the amplitude and phase of the SNR interferogram. Finally, an estimation model of vegetation water content and prediction model of the vegetation phase changes are established to modify the original phase and weaken the influence on the vegetation changes. Based on the corrected phase, a Genetic Algorithm Back Propagation Neural Network (BPNN) model is established for soil moisture inversion. According to the GPS monitoring data from the Plate Boundary Observatory H2O network, the experiment indicates that (1) The BPNN is introduced to inverse the soil moisture content, and the non-linear fitting ability of the model is well developed, and the fitting process is stable; (2) the modified phase effectively reduced the effects of vegetation changes on the soil moisture inversion. The correlation coefficient (r) between the inversion results and soil moisture value greatly improved, and the root mean square error and mean absolute error are less than 0.060 and 0.050, respectively. Therefore, the soil moisture problem can be treated as a non-linear event, and the algorithm is feasible and effective.

The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.

When producing high-precision seamless steel pipes, due to their large diameter to wall ratio and poor rolling stability, they mainly rely on subjective judgement and empirical control by operators, which can easily lead to quality defects, especially the problem of uneven wall thickness along the entire length. To alleviate the quality defects of seamless steel pipes, we propose a rolling force prediction modeling method for seamless steel pipe rolling mills that uses a hybrid differential evolution (DE) and grey wolf optimisation (GWO) algorithm optimised BP neural network (DE-GWO-BP), thereby improving the accuracy of rolling force prediction and the accuracy of seamless steel pipe wall thickness control. An offline model was established by collecting historical production data from a seamless steel pipe factory. Under the same data conditions, experimental comparisons were conducted with commonly used BP and GA-BP neural network algorithms in the rolling field. The experimental results indicate that the DE-GWO-BP model has the highest prediction accuracy, with higher prediction accuracy and performance than BP and GA-BP neural network models. Industrial field applications have shown that the DE-GWO-BP model effectively reduces the phenomenon of steel stacking caused by rolling force deviation, which plays an important role in reducing the quality problems of seamless steel pipes and improving the accuracy of seamless steel pipe wall thickness control.

The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 parameters that may affect the occurrence of complex downhole accidents, the mud overflow and leakage are predicted. The prediction results of the model are compared with the prediction results of a conventional BP neural network, and verified by the real drilling data. The results show that the MAE, MSE, and RMSE of the GA-BP neural network model are improved by 2.91%, 4.48%, and 10.93%, respectively, compared with the BP neural network model, and the prediction quality is higher. Moreover, the amount of mud overflow and leakage predicted by using the GA-BP neural network matches well with the pattern of mud overflow and leakage data in real drilling, which proves the effectiveness and accuracy of the GA-BP neural network in overflow and leakage prediction.

The Sixth International Symposium on Neural Networks (ISNN 2009)