Improved Back Propagation Neural Network Algorithm Research Articles

Machine Learning Algorithms for Predicting and Estimating Book Borrowing in University Libraries

Accurate prediction of borrowing volume of library books is conducive to the decision-making of the managers. This study briefly introduces the backpropagation neural network (BPNN) algorithm used to predict the borrowing volume of university libraries. The factor analysis method and genetic algorithm were employed to optimize the BPNN algorithm to improve its prediction performance. The book borrowing records of 2022 from Handan College Library were considered the subject of simulation experiments. The designed algorithm was compared with the extreme gradient boosting and traditional BPNN algorithms in the experiments. The results showed that average borrowing time, book lending ratio, book return ratio, and average grade of borrowers could be used as the input features of BPNN. The improved BPNN algorithm demonstrated faster convergence and a smaller error during training. The borrowing volume predicted by the improved BPNN algorithm closely matched the actual volume, and the increase in prediction time did not lead to a significant change in the prediction error.

Improved BP Neural Network Algorithm for Predicting Structural Parameters of Mirrors

Addressing the nonlinear correlations between input variables and output responses, in addition to the time-consuming nature of finite element analysis in mirror design, this study introduces an enhanced back-propagation (BP) neural network (BR-TLDBO-BPNN) employing Bayesian regularization and an optimized dung beetle algorithm. This novel approach facilitates rapid and efficient parameter estimations, significantly reducing the computational overhead. Utilizing an integrated analysis platform, the study obtained training and test samples, and the BR-TLDBO-BPNN model is used to predict the reflector’s mass and root mean square (RMS). The optimization mathematical model is built, and the nonlinear planning function (fmincon) is utilized to solve the problem and find an ideal set of structural parameters. The outcomes demonstrate that the prediction model is accurate enough to predict the mirror characteristics to optimize the mirror structural parameters. Empirical validation demonstrates that the proposed model achieves an over 99% accuracy in predicting mirror characteristics against finite element simulations. As a result, the BR-TLDBO-BPNN algorithm studied in this article not only broadens the application scope of neural networks, but also provides a new practical technique for engineering design.

Analysis and Prediction of Body Test Results Based on Improved Backpropagation Neural Network Algorithm

The mining and analysis of student achievement data is of great importance to teaching management. Using the relevant data of college students’ physical fitness test and sports performance as the object of research, a BP neural network model is developed to predict performance. Based on BP neural network, an algorithm for predicting students’ endurance performance is proposed, which is applied to the sunshine long-distance intelligent sports testing system at Hangzhou Dianzi University. The nonlinear relationship between students’ performance in sunshine running and endurance performance is determined, and students’ performance in sunshine running is used to predict their endurance performance the following year. Experimental results indicate that the accuracy of the model is above 85%. At the same time, the prediction results are combined with the Internet of things technology to produce a student sports prescription management system, which sets different sunshine running parameters for students with different predicted results and provides personalized sports prescriptions for students with different physical conditions, which has extensive and far-reaching application value.

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (BP) and improved BP neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., BP, GA-BP, and PSO-BP) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-BP and PSO-BP network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original BP model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.

Study on Early Warning on the Financial Risk of Project Venture Capital through a Neural Network Model

This paper aims to effectively reduce the financial loss of enterprises by accurately and reasonably making early warning of investment project risks. This paper briefly introduced the index system used for investment project risk early warning. It constructed a project investment risk early-warning model with a back-propagation neural network (BPNN) algorithm, and improved it with a genetic algorithm (GA) to solve the defect that the traditional BPNN is easy to fall into, over-fitting when reversely adjust parameters. An analysis was conducted on an electric power company in Hunan Province. Orthogonal experiments are performed to determine the population size and the number of hidden layers in the improved BPNN algorithm. The results showed that the improved BPNN algorithm had the best performance when the population size was set as 25 and the number of hidden layers was four; compared with support vector machine (SVM) and traditional BPNN algorithms, the GA-improved BPNN algorithm had better performance for early risk warning of investment projects. In conclusion, adjusting the parameters of a BPNN with a GA in the training stage can effectively avoid falling into over-fitting, thus improving the early warning performance of the algorithm; in addition, the improved BPNN has better early warning performance.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

An Improved BPNN Algorithm Based on Deep Learning Technology to Analyze the Market Risks of A+H Shares

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

Inverse kinematics solution of Robotics based on neural network algorithms

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.

Research on Classification Method of Vehicle Body Surface Defects Based on BP Neural Network

In order to classify the surface defects of the vehicle body, a classification method based on BP (back propagation) neural network is proposed. According to the characteristics of vehicle body defects, the gray, morphological and geometric features of various defects were extracted, and the defect feature database was established. The defect classifier was designed by using improved BP neural network algorithm. The experimental results show that the method is very feasible and effective in the identification of vehicle body defects.

Improved BP neural network algorithm to wind power forecast

To constantly enhance the accuracy of wind power prediction and furthermore reduce the uncertainty of power grid dispatching, this study proposes an improved back propagation (BP) neural network algorithm. The original prediction method of BP neural network algorithm has been improved, and the traditional minimum square error (SE) perform function is abandoned. Maximum correntropy criteria (MCC) algorithm which is more conducive to deal with non-Gaussian error and big noise is introduced, and a new perform function is created. Through the analysis of examples, the feasibility of MCC algorithm is verified. Comparing to the traditional mean SE (MSE) perform function, MCC perform function could drop the limit error of prediction, reduce root MSE and increase the correlation between forecasting power and real power. The most important is that the prediction accuracy is enhanced.

Investigation on surface morphology model of Si3N4 ceramics for rotary ultrasonic grinding machining based on the neural network

Si3N4 ceramics parts surface morphology is related with surface friction and wear properties directly. Poor surface morphology will result in friction coefficient increases, strength decreases, and even lead to component failures. In order to improve Si3N4 surface morphology, it is necessary to investigate on the relationship model between the surface morphology and process parameters. In the paper, rotary ultrasonic grinding machining (RUGM) was taken as object to establish the model based on back propagation (BP) neural network. However, the nonlinear relationship of the model is complex, and the traditional algorithm cannot realize satisfying results. So an improved BP neural network algorithm based on Powell method has been proposed. The paper gives the theory and calculation flow of the algorithm. It is found the algorithm can accelerate the iteration speed and improve iteration accuracy. The investigation results provide the support for surface morphology optimization.

Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application

Aiming at reducing the losses from flood disaster, a dynamic risk assessment model for flood disaster is studied in this article. This model is built upon the projection pursuit cluster principle and risk indexes in the system, proceeding from the whole structure to its component parts. In this study, a fuzzy analytic hierarchy approach is employed to screen out the index system and determine the index weight, while the future value of each index is simulated by an improved back-propagation neural network algorithm. The proposed model adopts a dynamic evaluation method to analyze temporal data and assesses risk development by comprehensive analysis. The projection pursuit theory is used for clustering spatial data. The optimal projection vector is applied to calculate the risk cluster type. Therefore, the flood disaster risk level is confirmed and then the local conditions for presenting the control strategy. This study takes the Tunxi area, Huangshan city, as an example. After dynamic risk assessment model establishment, verification and application for flood disasters between the actual and simulated data from 2001 to 2013, the comprehensive risk assessment results show that the development trend for flood disaster risk is still in a decline on the whole, despite the rise in a few years. This is in accordance with the actual conditions. The proposed model is shown to be feasible for theory and application, providing a new way to assess flood disaster risk.

An Improved BP Neural Network Algorithm and its Application

Back-propagation (BP) neural network algorithm is currently used most widely and grows fastest for its powful nonlinear simulation capability. However BP neural network is so easy to fall into local minima that it cant find the global optimum which limits its application in many fields. The paper, taking tax innovation teaching evaluation for example, advances a new evaluation algorithm based on improved BP neural network algorithm. Firstly an evaluation indicator system of tax major innovation teaching is designed through analyzing the specific characteristics of innovation teaching requirements. Secondly, in order to overcome the shortages of low convergence speed of original BP neural network algorithm, the paper improves BP algorithm through integrating BP algorithm and ant colony algorithm, ant improving the overall search method of integrated algorithm. Thirdly data from three universities are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate university innovation teaching practically.

Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data

The study of light distribution in orchards is very important for enhancing agricultural production. Nonlinear massive data, amounting to more than 190MB, were collected over a 6-month period. Information such as the location, illumination, and time was obtained from wireless sensor networks, while that of canopy density and slope aspect was obtained through manual surveys. This paper proposes an improved back propagation (BP) neural network to study sunshine illumination distribution by exploiting these data. The basic BP neural network is divided into Q groups, each of which receives R samples and is trained individually using a gradient descent algorithm. Every grouped neural network records its error at the end of each training round. The new weights and thresholds, selected according to these error values, are employed in the next round of training, and the training process does not terminate until the error is within the desired goal. Finally, to verify the validity of the algorithm according to various criteria, the improved BP neural network is used to study sunshine illumination in an orchard. Our experiments show that the improved BP neural network algorithm performs better than traditional algorithms including the spline interpolation, Kriging, and basic neural network algorithms, and yields an accurate sunshine illumination distribution that can be used to improve agricultural production.

An Improved BP Neural Network Algorithm Based on Factor Analysis

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. Factor analysis (FA) is a multivariate analysis method which transforms many feature variables into few synthetic variables. Aiming at the characteristics that the samples processed have more feature variables, combining with the structure feature of BP neural network, a FA-BP neural network algorithm is proposed. Firstly we reduce the dimensionality of the feature factor using FA, and then regard the features reduced as the input of the BP neural network, carry on network training and simulation with low dimensional data that we get. This algorithm here can simplify the network structure, improve the velocity of convergence, and save the running time. Then we apply the new algorithm in the field of pest prediction to emulate. The results show that under the prediction precision is not reduced, the error of the prediction value is reduced by using the new algorithm, and therefore the algorithm is effective.