Improved BP neural network algorithm to wind power forecast

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.

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

The rapid development of artificial intelligence technology makes Robotics more intelligent and flexible. In this context, the kinematics inverse algorithm of the Robotics has become the basis and key technology for the further development of the Robotics. The key point of the inverse algorithm of the Robotics is to coordinate the Robotics operation arm with the corresponding action actuator at the end to realize the space attitude control of the Robotics system, and to make a theoretical basis for the motion analysis of the later Robotics. However, the traditional form of Robotics kinematics inverse algorithm avoids a lot of iterative computational solution process, which increases the complexity of the whole algorithm. Therefore, based on the above situation, this paper proposes a Robotics inverse solution algorithm based on improved BP (back propagation) neural network. In this paper, in the application of the actual algorithm, aiming at the convergence problem of the traditional BP neural network algorithm, an improved BP neural network algorithm based on the excitation function is proposed. By selecting the adaptive processing function in each layer of the neural network, the selection is matched with it. The learning rate, thus improving the accuracy of the entire motion inverse algorithm. At the same time, in order to further reduce the calculation of joint quantification, this paper also creatively introduces the algorithm of plane division auxiliary dynamic model construction. The simulation results show that the inverse kinematics algorithm based on improved BP neural network proposed in this paper has obvious advantages in solving the kinematics inverse problem of six-degree-of-freedom Robotics compared with the traditional inverse solution algorithm.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Relational model of compositions of enamel coatings and sintering temperature based on back propagation neural network algorithm

Pavement cracks are an important hidden danger for driving safety. Rapid and accurate detection of road crack types and effective treatment can greatly reduce the incidence of traffic accidents, which has important practical significance. Based on the advantages of scale invariant feature transform (SIFT) and back-propagation neural network (BP) algorithm, this paper proposes a method of road crack detection and recognition for four types of cracks, such as cracks, longitudinal cracks, transverse cracks and massive cracks.Firstly, the crack image is denoised and enhanced; Then scale invariant feature transform method is used to extract the feature point information of the route crack image; Finally, Back propagation neural network is used for training and recognition. The results show that this method can quickly and accurately identify the crack type, in which the accuracy of training set is 98.79%, and the accuracy of recognition set is 86.47%. The results show that the combination of scale invariant feature transform and back propagation neural network algorithm can effectively improve the efficiency of pavement crack type detection.

This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing. The results demonstrate that the proposed model effectively identifies factors contributing to supply chain deterioration, with a warning error as low as 0.001, significantly enhancing the accuracy and timeliness of demand forecasting. The BPNN algorithm, through its self-learning and adaptive features, facilitates dynamic optimization and precise scheduling across various stages of the supply chain. This capability is particularly valuable in addressing challenges associated with sudden demand spikes and resource allocation. As a result, the mechanism is able to accurately and promptly identify adverse trends in the supply chain, thereby enhancing the efficiency and flexibility of urban emergency responses, mitigating risks, and offering both theoretical and practical contributions to urban collaborative governance.

In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal.

A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method using a traditional back-propagation (BP) neural network algorithm, the improved grey wolf optimization (IGWO) algorithm has been adopted to optimize its parameters. The performance of the proposed method has been evaluated by experiments. First, the features of the wind farm are described to show the fundamental information of the experiments. A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines. Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points. Then, the retained wind power data were predicted and analysed by using the proposed IGWO–BP algorithm. Analysis of the results proves the practicability and efficiency of the prediction model. Results show that the average accuracy of prediction is ~11% greater than the traditional BP method. In this way, the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy. A short-term wind power prediction method is designed and tested with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method, the improved grey wolf optimization algorithm has been adopted to optimize its parameters.

In order to improve the prediction accuracy of the back propagation (BP) neural network model, a prediction model is presented based on the combined adaptive boosting (AdaBoost) and the back propagation neural network algorithm. The efficiency of the proposed prediction model is proved by predicting the college graduates employment. The computer simulations have shown that this model is effective and suitable. It has higher prediction accuracy and is applicable to practice.

Carbon emission is a significant indicator to balance regional development and environmental pollution. Currently, to achieve the global goal of carbon neutrality, many countries had formulated relevant policies to control carbon emissions in the future. It is of great importance to solve the problem of mid-term and long-term prediction of regional carbon emissions. To improve the prediction accuracy and lower the calculation cost, this paper uses the Principal Component Analysis (PCA) method to reasonably screen the driving factors of carbon emission prediction. It combines the Back Propagation (BP) neural network and Genetic Algorithm (GA) to construct the Genetic Algorithm - Back Propagation neural network (GA-BP) model for mid-term and long-term prediction. Based on historical statistics and official policy indicators, the prediction of carbon emissions in Jiangsu Province of China from 2025 to 2050 is carried out. By comparing the prediction results of the Multiple Linear Regression (MLR) model and the classic BP model, the improved GA-BP model based on Principal Component Analysis Stochastic Impacts by Regression on Population, Affluence, and Technology (PCA-STIRPAT) is significantly better than other methods in the mid-term and long-term prediction of regional carbon emissions, with a maximum relative error of 0.76% and a high prediction accuracy. This result can provide a reasonable basis for relevant parties to make decisions on controlling carbon emissions.

In this paper the back propagation (BP) neural network algorithm is applied to predict the traffic flow of urban road. The neuron structure needs 48 input nodes and 48 output nodes, so the frame of 48-20-48 is selected. First train an ideal input network with lower error square sum, then take the trained weight vector as initial value of the next input vector. The network training is realized by functions of adaptive learning rate and additional momentum method. The design can forecast 5-minute vehicle flow in future by the current related traffic flow and provide effective information for traffic department. The simulation by Matlab shows that the method with power learning ability and adaptability has high application value.

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

Physical education at college is the final step of school physical education, and it is critical to a student's educational experience. There are numerous issues such as the integration of existing teaching resources, combining the characteristics of basketball teaching to improve teaching quality, and the use of basketball as a means of lifelong physical exercise faced by college basketball teachers. To address these challenges, this research attempts to integrate the backpropagation (BP) neural network technique with its enhanced deep learning algorithm by adjusting the learning rate, adding more momentum items, increasing the excitation function, and other methods. A teacher’s effectiveness as a teacher is examined using numerous variables utilizing the suggested BP neural network assessment model for measuring teaching quality. In addition, the specific implementation of the improved BP neural network algorithm includes the determination of the number of BP neural network layers, the input data preparation method, and the number of neurons per layer. Finally, the simulation experiment evaluates the teacher’s teaching quality using the upgraded BP neural network.

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.