Output prediction about crosstalk coupling of representative multi-cable bundle on aircraft platform based on neural network

In view of the problem that it is difficult to predict the output in an oilfield which affected by multiple variables, a back propagation (BP) neural network model is built to predict the output in oilfield because the classic statistical method and static model can not meet the demand of precision for the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are used as the input of neural network and oilfield output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy. The results show that the model can forecast the oilfield output with accuracy comparable to other classic methods. So the BP neural network is an effective method to predict the oilfield output with high accuracy. The application of this approach can supply reliable data for the development of oilfield and decrease the risks for the exploitation.

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.

Considering that vehicle leaf-spring has the typical characteristics of combination, a method of structure configuration is put forward in configuration design for vehicle leaf-spring. The main problem of structure configuration is described aiming to the feature of configuration design for vehicle leaf-spring. Through the generalization of structure type of leaf-spring and the corresponding design requirement, output and input of Neural Network is built and coded in binary. Meantime, in order to decrease the number of sample and simplify the process of sample train and the structure of network, two BP Neural Networks are built to solve the structure features of cross section & section and leaf end & spring eye respectively, which are trained by helps of Neural Network tool in Matlab 7.1. The result of simulation proves that the BP model can meet the demand of solving structure configuration for vehicle leaf-spring.

The Sixth International Symposium on Neural Networks (ISNN 2009)

This paper presented a hybrid neural network model to integrate information entropy theory and ant colony clustering for load forecasting. As short-term load forecasting is a complex problem with multifactor in power system, if all these factors are used as inputs of neural network, it will not only result in complicated network structure, but also long learning time and inaccurate prediction. First, information entropy theory is used to select relevant ones from all load influential factors, the results are used as inputs of neural network. It can reduce irrelevant load influential factors and the input variables of the input layer for neural network. Next, considering the features of power load and reduced influential factors, using ant colony clustering method, the practical historical load data within one year is divided into several groups. A separate module based on neural networks models each group. Then, the typical samples in each clustered group were selected as the training set for the separate improved Elman neural network which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. According to the procedures, the reduced input variables and the typical training samples for each neural network can be gotten. Thus the neural network forecasting model based on information entropy and ant colony clustering can be constructed which can effectively reduce the training time and improve convergent speed. During the forecasting process, pattern recognizing is employed to activate the corresponding module for hourly load forecasting. The presented model was tested using Hebei Province daily load data, and the satisfactory results were obtained.

In view of difficulty to predict the output in oilfield which affected by multi-variables, RBF neural network model is set up to predict the output in oilfield because the classic statistics method and static model can not meet the demand of precision to the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are as the input of neural network and oilfield output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy. The results show that the model can forecast the oilfield output with accuracy comparable to other classic method. So the RBF neural network is an effective method to predict the oilfield output with high accuracy. The application of this approach can supply reliable data for the development of oilfield and decrease the risks for the exploitation.

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

In order to improve the accuracy of port container throughput prediction, an improved ABC-BP prediction model based on Artificial Bee Colony (ABC) was proposed. With the advantage of global search ability and difficulty in local optimization, the weights and thresholds of Back Propagation (BP) neural network are optimized, and the optimal weights and thresholds in the network are finally determined. Referring to the existing research results, Qingdao 2014-2019 (24 quarters) GDP, port throughput, total foreign trade import and export volume were selected as network input, and Qingdao port 2014-2019 port container throughput statistics were used as network output, a total of 24 sets of data were constructed, so as to build a BP neural network prediction model. The first 20 groups of data were used as the training set, and the last 4 groups of data were used as the test set for instance verification. The results show that, compared with the traditional BP neural network and the BP neural network prediction model optimized by Genetic Algorithm (GA), the ABC-BP model can significantly improve the prediction accuracy of port container throughput.

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

In sea battlefield search and rescue operation problems, the risk assessment for ships is a vital procedure to get a decision support as an important intelligence to combat search and rescue. In this paper, based on back propagation (BP) neural network, a risk assessment indicator system is proposed first to facilitate the hazard, the vulnerability, and the resilience. Next, by developing two types of network structure, the main neural network for estimating the risk has been modeled. Then, the corresponding learning algorithm is designed. Finally, 69 samples for search and rescue ships are given as the input of the neural network. The performance and regression state of the obtained training results demonstrate that the proposed risk assessment method is valid and effective.

An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.

Accurate and rapid detection of soluble solids content (SSC) and pH is important for fruits and vegetables. This study achieves this objective by developing advanced modeling techniques. Initially, a Partial Least Squares Regression (PLSR) model was established using one-dimensional (1D) spectra (wavelength: 350-2500 nm) to assess the feasibility of predicting SSC and pH in grapes and cherry tomatoes. Subsequently, various regression models were developed based on 1D spectra, including Back-Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Convolutional Neural Network (1D-CNN). Comparative analysis revealed that the 1D-CNN model exhibited the best performance. To further enhance model accuracy and generalization, the 1D spectra were transformed into two-dimensional (2D) domain feature matrices, representing the relative relationships between different spectral bands. These 2D matrices were then used as input for a 2D Convolutional Neural Network (2D-CNN) regression model, which demonstrated superior prediction accuracy and generalization capabilities compared to the 1D-CNN. Additionally, a frequency feature matrix was extracted by applying a sliding window technique combined with wavelet transform to the spectral data, capturing the amplitude-frequency characteristics. By integrating both the 2D domain feature matrices and the 2D frequency feature matrices, a dual-channel convolutional neural network (Dual-CNN) was constructed. The Dual-CNN model exhibited improved precision, stability, and generalization performance compared to previous models. The efficacy of the proposed approach was validated through the detection of SSC and pH in grapes and cherry tomatoes. The determination coefficients of prediction (RP2) reached 0.958 and 0.929 for SSC and pH, respectively, across five grape cultivars. Similarly, for cherry tomatoes, RP2 values of 0.936 and 0.925 were achieved for SSC and pH predictions across five cultivars. Furthermore, small-scale model transfer validation between grapes and cherry tomatoes demonstrated robust performance, with RP2 values ranging from 0.892 to 0.915 for SSC and pH predictions. These findings highlight the potential of the proposed method in enhancing the accuracy and reliability of spectral detection for assessing the quality attributes of fruits and vegetables.

An estimation method for free bolt stress is described. Acoustic stress wave signals of free bolt were collected under different tensile forces and analysed in time domain and frequency domain after cross-correlation. The variations of wave propagation time, fundamental and secondary frequency of signals' spectrum are studied. Then signals are decomposed into intrinsic mode functions (IMFs) by ensemble empirical mode decomposition (EEMD). The normalised energy ratios and correlation coefficients of IMFs are also discussed. Propagation time, fundamental and secondary frequency of signals' spectrum, energy ratios and correlation coefficients of IMFs are influenced by applied tensile force. Thus they are selected as the components of eigenvector for inputs of neural network. Back propagation neural network (BPNN) and genetic algorithm (GA) optimised BPNN are used for tensile force prediction. Eleven sets of data were used to test the stress prediction effect of BPNN after training. The results indicate that the BPNN optimised by GA can achieve small errors for stress prediction.

Compressive creep of corundum-mullite refractories is an important performance to measure whether they can be used stably for a long time at high temperature. Therefore, a compressive creep prediction method of corundum-mullite refractory based on BP neural network is proposed. The inputs of the BP neural network are the content of Al2O3, SiO2, Fe2O3 and the time of high temperature whereas creep rate is the output. The results show that the predicted results can correspond well with the experimental results. The developed BP neural network model can efficiently and accurately predict the creep rate of corundum-mullite refractories.

Fiber Bragg Grating (FBG) sensor is a new type of optical sensor that is light in quality, small in volume, easy to achieve distributed measurement, and easy to embed. When FBG sensor technology is introduced to the damage detection of the CFRP shaft, the shortcomings of conventional damage detection technologies, including ultrasonic testing and radiographic testing, can be effectively overcome. Considering the working load, operating environment and life requirements of the CFRP shaft , combined with the relevant national and industry standards for composite material testing, Prepare three CFRP shaft with embedded fiber gratings (non-destructive CFRP shaft, single hole CFRP shaft , double hole CFRP shaft), the hole diameter is 1mm, and the layering of CFRP shaft is ([0 / 90 / 45 / 0 / 0 / 45 / -45 / 0 / -45 / 0 / 90 / 90 / 0 / -45 / 0 / -45 / 45 / 0 / 0 / 45 / 90 / 0 ]). FBG is embedded between 18 and 19 layers, pasted on the surface of CFRP shaft with 10 FBG sensors (named FBG1, FBG2... FBG10). The strain information of the CFRP shaft is obtained through fiber grating to obtain the deformation of the CFRP shaft under a three-point bending load. Each CFRP shaft is loaded 12 times (A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3). Through the BP neural network method, the mapping relationship between strain distribution and damage model was established. In the model, the FBG sensor measures the strain of the CFRP shaft and forms the input of the neural network. Combined with the damage situation of the composite material, the learning samples of the network formed. After learning and training, the neural network can damage recognition. To better verify the accuracy of the neural network to distinguish and identify the CFRP shaft non-destructive, single-hole, and double-hole damage patterns. The finite element analysis was used to simulate the damage of three CFRP shafts under the same experimental conditions, and extract the eigenvalues at the same position to form the input of the network, combined with the damage situation of the composite material, constitutes a learning sample of the network. The experiment shows that the CFRP shaft experiment based on BP neural network verifies the reliability of the CFRP shaft damage recognition method. The finite element analysis is used to extract the strain information of CFRP shaft measuring points with different damage modes. The damage position information and strain information were input into the BP neural network. The experimental results are in good agreement with the simulation results, which verifies the accuracy of the BP neural network to distinguish the CFRP shaft non-destructive, single-hole, and doublehole damage patterns.