LM-BP Neural Network Research Articles

Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions

The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.

Construction of Evaluation System for English Translation Teaching in Colleges and Universities under Cross-cultural Perspective

Abstract The construction of the evaluation system of English translation teaching in colleges and universities under the cross-cultural perspective to improve the quality of translation education. The focus is on integrating the LMBP neural network algorithm and quantitative judgment indexes to provide an adequate evaluation model for English translation teaching. The study adopts the neural network algorithm based on LMBP, combined with the quantitative processing methods of “AHP+DEA” and “Entropy Assignment Method+Euclidean Distance Method”, to systematically construct and empirically analyze the evaluation indexes of teaching. It is found that teaching construction, application and effect are the core indicators of evaluation. Regarding teaching construction, classroom content integrity (correlation coefficient 0.428) and learning resources integrity (correlation coefficient 0.439) are the key factors. Teaching application analysis showed that hybrid teaching positively correlated with student achievement (r=0.569). Conducting effectiveness analysis showed that student discussion and in-class performance were significantly and positively correlated with teaching evaluation (p<0.005). These results prove the effectiveness and usefulness of the constructed evaluation system.

Research on stage–discharge relationship model based on random forest algorithm

Hydrological simulations and predictions are vital aspects of hydrological change research. Accurate predictions of hydrological factors such as stage and discharge are essential for water resources planning, reservoir dispatching and operation, shipping management and flood control. River discharge forecasting during flood seasons is an important issue in water resources planning and management. To improve the calibration accuracy and stability of the stage–discharge relationship model, the feasibility of integrated algorithms for studying the stage–discharge relationship was explored. A random forest (RF) algorithm based on a neural network (NN) was developed using a framework of integrated algorithms. First, the Levenberg–Marquardt (LM) algorithm was used to optimise the weight updating process of a back-propagation (BP) NN and improve the convergence speed. Then, the LM-BP algorithm was used as a decision tree to build an RF algorithm. The model was tested using hydrological data from Hongqi station on the Dadu River in China in the flood season. Results for the classical model, BP NN model, LM-BP NN model and optimised algorithm model were evaluated based on the mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The performance indicators showed that the optimised algorithm model (MAE = 3.13 m3/s, MSE = 19.28 m3/s and MAPE = 1.8%) was superior to the other models and showed high accuracy and good stability in flood-season flow forecasting.

Research on Improved BP Neural Network Prediction Based on Niche Particle Swarm Optimization Algorithm

As for data prediction, a prediction model is proposed based on BP neural network. This method is based on the traditional BP neural network algorithm to improve the problems of slow convergence speed and easy to fall into local optimum. The idea of niche is introduced into the particle swarm optimization algorithm to improve the global search ability and convergence speed, find the global optimum value, and then use LMBP neural network to continue to optimize the parameters to improve the prediction effect.

Analysis of Runoff Coefficient Prediction Based on LM-BP Neural Network

Runoff coefficients are affected by many factors, and their complex nonlinear relationships make it difficult to calculate accurate runoff coefficients using experimental physical models. In this paper, we improved the traditional BP neural network model based on the Levenberg-Marquardt method and established an S-type/S-type mathematical model of the relationship between runoff coefficients and influencing factors to predict each surface runoff coefficient under different rainfall conditions and different subsurface conditions, and compared it with other methods. The results calculated after the actual case simulation showed that the error of LM-BP neural network simulation was within the range of 0.03~0.09, the error was smaller, the calculation results were more accurate, and the prediction of runoff coefficient had the advantages of strong generalization ability and high prediction accuracy, which was a great improvement to the traditional rainfall-runoff coefficient best-fit relational fitting relationship method. Besides, in order to reduce the problem of sample overtraining, the more detailed the hydrological information, the better.

Rural Planning Evaluation Based on Artificial Neural Network.

The continuation of human civilization is inseparable from the development and construction of rural areas, and infrastructure is the core of rural development. China has been building large-scale rural infrastructure in recent years. Rural infrastructure building, for example, is huge in both quantity and scope, but it is beset by challenges in its current construction and development, and it urgently requires suitable leadership. Planning assessment, as a technical method, can identify problems in regional development and is a powerful tool for evaluating the impact of planning and construction and promoting the development of complete new areas. This paper is aimed at the planning evaluation of rural construction and the evaluation of rural construction and guides the planning and implementation of the next step of rural construction, to assist China's supervision and inspection of rural construction effect and promote rural construction and development into a good track. In view of the low accuracy and efficiency of the current evaluation model of rural planning and the problem that a single neural network easily produces local extreme value, the neural network method is improved, and the application of LM-BP neural network in the evaluation model of rural planning is proposed. Input sample elements are five factors affecting rural construction, including industrial construction, population distribution, and utilization rate of large-scale facilities, construction of public facilities, and promotion effect of supporting policies. Output sample is the evaluation result. On this foundation, the LM-BP neural network was used to convert the training into a least square problem, and the LM method was used to redefine the number of hidden layer nodes, resulting in the construction of a rural planning evaluation model based on the LM-BP neural network. This approach is used to determine the outcomes of rural planning evaluations. The experimental results show that the designed evaluation model has a small evaluation error, has the advantage of high accuracy compared with similar models, and is a reliable evaluation model for rural planning.

Prediction method of energy consumption for high building based on LMBP neural network

With the power consumption of high-rise buildings rising year by year, energy consumption of high-rise buildings has become the main object of energy-saving supervision and transformation, and its prediction has become a research hotspot. Aiming at the problem that the existing total energy consumption forecasting methods cannot accurately distinguish the consumption direction of high-rise building and the forecasting accuracy is low. LM algorithm is used to improve the standard BP neural network, and a prediction model of lighting energy consumption for high-rise buildings based on LMBP neural network is established. According to the prediction results of lighting energy consumption, hourly average outdoor temperature, hourly average outdoor relative humidity, weather characteristics, holidays, hourly average wind speed and 24 hourly hours a day, the energy consumption of air conditioning, power consumption and special energy consumption can be predicted separately. The experimental results show that the MAE and RMSE predicted by the design model for air conditioning energy consumption, power energy consumption and special energy consumption are 5.4707 and 8.6575, 1.7916 and 2.9356, 1.6075 and 2.2178 respectively, which are smaller than the comparison model MAE and RMSE, which proves that the design model can more accurately and effectively predict the sub item energy consumption in the energy consumption of high-rise buildings.

Reverse Logistics Enterprise Performance Research Based on Super-Efficiency DEA and LMBP Neural Network

On the basis of considering the characteristics of reverse logistics enterprises, this paper uses stakeholder theory to establish an evaluation system containing 36 indicators in five dimensions, based on the balanced scorecard, and adopts PCA optimization index. In view of the performance evaluation problem, first, the performance data is obtained by using the super-efficiency DEA, then the enterprise performance of A company is fitted by LMBP neural network to obtain the quantitative model of performance evaluation, and finally, according to the resulting weight reference performance benchmark, the direction of reverse logistics enterprise performance improvement is given. The simulation results show that the index system can react to the reverse logistics characteristics very well, and the BP neural network based on LM algorithm can accurately and efficiently evaluate the performance of reverse logistics enterprises which has better generalization, and can find out the key performance indicators and give reasonable optimization direction for enterprises by reverse tracking the weights of the neural network.

Water Quality Analysis and Evaluation of Eutrophication in a Swamp Wetland in the Permafrost Region of the Lesser Khingan Mountains, China.

Wuyiling Nature Reserve is located at the edge of a frozen soil area and has abundant vegetation resources. It is an important area for evaluating the impact of frozen soil degradation on the environment. Analyzing the water quality and eutrophication characteristics of different swamps and water bodies can provide a basis for protecting the water environment of frozen soil areas. The pollution characteristics of different swamps and water bodies were analyzed and the Levenberg-Marquardt back-propagation neural network was used to evaluate water quality and eutrophication. Finally, the eutrophication evaluation was compared with the nutritional status index. The results demonstrated that (1) the highest concentrations of the total phosphorus (TP), total nitrogen, ammonia nitrogen (AN), permanganate index (PI), chemical oxygen demand, and chlorophyll were all present in the Tangwang River, which could be caused by the effects of human and agricultural activities along the river. The maximum average concentrations of TP, AN, and PI in the four wetland swamps appeared in the shrub swamp; (2) The water quality category of shrub, grass, and forest swamps in Wuyiling Nature Reserve was Class II, and the water quality category of floating swamps and the Tangwang River was Class I. The nutrient levels of the four marsh swamp wetlands and the Tangwang River were in the mid-range for many nutrients; and (3) the LM-BP neural network model, which was used to evaluate water eutrophication, and the results of a comprehensive evaluation of a nutritional status index were similar, further demonstrating the credibility of the evaluation results.

Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m2 and −1.59 W/m2, respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.

Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

Diffusion Test and Prediction of PM2.5 Concentration in Urban Street Traffic Microenvironment

A detailed test plan was developed by analyzing the factors affecting PM2.5 diffusion in the urban street traffic microenvironment. After field investigation, several test streets within the third Ring Road of Harbin city were selected to investigate and test the hourly traffic flow of the streets, and the geometric structure of the block (road width, building height) was measured. The research carried out tests on the PM2.5 concentration, wind speed, temperature and relative humidity of actual street test points. Based on the actual test data, BP artificial neural network and the improved LMBP neural network were used to carry out simulation research on MATLAB platform respectively. A PM2.5 concentration prediction model was established to compare and analyze the reliability of the model. Scientific and reasonable prediction of PM2.5 pollution in the traffic microenvironment in a specific area.

Underwater Detection Signal Based on LM-BP Neural Network Algorithm

The algorithm of underwater signal detection based on LM-BP neural network has a very broad application prospect. It is an important means of underwater background weak signal detection and a good method of phase space reconstruction. In this paper, the band-pass LM algorithm combined with BP neural network is used to process the underwater signal, which can effectively filter the magnetic noise in the underwater environment and extract the characteristics of the underwater detection signal. This paper first analyzes the time-frequency characteristics of the underwater detection signal, then studies the BP neural network of LM algorithm, and studies the design of band-pass filter, finally analyzes the application example based on LM-BP algorithm.

Fault Diagnosis of HVAC System Considering LMBP Neural Network Method

Given the complexity of the mathematical model for the HVAC system and the constant changes of multiple external disturbances and working conditions, the application of intelligent control in the HVAC control system to improve the control performance of the HVAC control system has become a research hotspot. The Levenberg-Marquart algorithm (LM algorithm) is studied to address the defects of slow convergence speed and prone to falling into local minimum in the learning process of the BP neural network. To solve the two issues (the choice of the learning rate, and the solution of the inverse matrix) that significantly affect the training time and convergence accuracy, we improved and optimized the LM algorithm using the LU decomposition method and implemented the algorithm through the MATLAB programming language. The LMBP neural network method obtained is applied to the control loop of the HVAC chilled water cycle. The simulation comparison study is performed on its control effect and that of the PID control algorithm and BP neural network PID control algorithm. The research results show that the LMBP neural network method has significantly improved the performance in aspects such as reducing overshoot, accelerating convergence speed and reducing steady-state error, etc.

Risk Assessment of Power Communication Network Based on LM-BP Neural Network

In order to predict the operational risk of the power communication network more comprehensively and accurately, this paper presents a BP neural network model based on Levenberg-Marquardt (LM) algorithm. Firstly, establishing a set of indicators that reflect the operational characteristics of the power communication network. These indicator data are used as input to the model. Then, the operating efficiency of the traditional BP neural network is improved by the LM algorithm. The research results show that the model is simple, the forecast performance is stable and the accuracy is high, which provides an effective theoretical basis and modeling method for the prediction of power communication network risk.

Research on Prediction Model of Line Loss Rate in Transformer District Based on LM Numerical Optimization and BP Neural Network

Due to the complex network structure of the low-voltage distribution network, the backwardness of electrical equipment and the large number of nodes, it is difficult to calculate the line loss rate accurately. The author established a simple linear loss rate prediction model with higher accuracy. Firstly, according to the actual power operation data, the typical characteristic parameters with great influence are obtained. Secondly, the K-means clustering algorithm is applied to classify each transformer district according to the different power consumption characteristics. Finally, the BP neural network model based on LM numerical optimization algorithm is designed and applied to predict line loss rate for data class of each transformer district. 1026 transformer district with complete power operation data in a region was selected as an example to verify the accuracy of the proposed prediction method, which provides data and theoretical basis for the loss reduction measures.

Computer Network Security Evaluation Based on LM-BP Neural Network

The assessment of computer network security is a significant system in computer network security assurance. Aiming at the shortcomings of BP neural network in network security evaluation, such as the slow convergence speed, the difficulty of globally optimal solution, the low accuracy of diagnosis and the uncertainty of network structure. Taking all these disadvantages into account, this paper aims to revise BP neural network by using levenberg-marquardt algorithm and combine the actual sample data to operate simulation test. As a result, LM-BP neural network algorithm, which possesses the advantages of fast learning speed and strong generalization ability, provides an effective, accurate and reliable method for the evaluation of computer network security.

Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Abstract. Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline.

Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network

With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value.

Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.