Use Of BP Neural Network Research Articles

Research on artificial intelligence machine translation based on BP neural algorithm

The primary focus of artificial intelligence advancement is in machine translation; nonetheless, a prevalent issue persists in the form of imprecise translation. The current challenge faced by artificial intelligence is to effectively executing machine translation from extensive datasets. This research presents a BP neural method that aims to repeatedly analyse translation data and achieve optimisation in machine translation. The findings indicate that the use of BP neural network may enhance the dependability and precision of machine translation, with an accuracy rate over 84%. This performance surpasses that of the online translation approach. Hence, it can be inferred that the use of BP neural algorithms has the potential to fulfil the requirements of machine translation and enhance the precision of online translation conducted by humans.

Research on human resource management performance evaluation based on BP algorithm

Abstract With economic globalisation and in the face of increasingly fierce market competition, enterprises must establish an evaluation mechanism that can promote the company’s development and motivate employees to improve performance in order to achieve the company’s strategic goals. In view of the characteristics and problems of enterprise performance appraisal, a performance evaluation method based on artificial neural network (ANN) technology is proposed. This study uses BP algorithm to comprehensively evaluate the performance of enterprises and construct an evaluation network. According to the statistics of 75 power companies in the province from 2018 to 2021, the training was carried out in batches, and the 10-fold cross-validation method was used to find the smallest optimisation value of the error term (average overall relative error) of the test set. The training set is set as 70% and the test set as 30%, and the termination condition is used to end the training process when the training error is <0.0001. This proves that the use of BP neural network for performance evaluation can effectively avoid the influence of subjective factors on the evaluation results, so as to establish a more objective comprehensive evaluation system.

Review of swarm intelligence algorithm optimization of BP neural network

As a model used for parameter estimation, BP neural network has a remarkable effect in many prediction algorithms. However, there are situations that will fall into the local optimal solution and the learning speed is slow. To solve these two problems, this paper combines the dynamic adaptive strategy in the genetic algorithm, the method of eliminating honey sources in the bee colony algorithm, the adaptive greedy strategy in the ant colony algorithm, and the introduction of variation operator in the particle swarm algorithm to improve the two defects of the BP neural network, It has made optimization research on the use of BP neural network and prediction, and also laid the groundwork for the future research on optimization of BP neural network.

RETRACTED ARTICLE: Real-time detection of aerobics posture based on strain sensor

The Internet of Things is a network that realizes the intelligent connection between things. It is another major industry after the computer industry and the Internet industry. It is also a collection of computer technology, communication technology, sensor technology, storage technology, and many other leading technologies in an integrated industry, the application industry is very wide. This article mainly introduces the real-time detection of aerobics posture based on strain sensors. This paper proposes a real-time detection method of motion posture based on strain sensors, using flexible rods to simulate the joints of aerobics athletes. Through the survey of the motion posture by the strain sensor, the relevant data and parameters are collected, and then the data are processed, and the data are finally transmitted to the terminal. Since the stress of a specific point on the surface of the flexible rod is proportional to the curve of that point, strain gauges can be used to detect curve changes at multiple points in real time and draw the change curve on the computer. The experimental structure of this paper shows that the use of BP neural network to process the measurement data improves the accuracy of real-time detection. The error distance between the sampling point and the actual value is less than 0.3 cm. In addition, through the real-time detection of the motion posture, it can be concluded that good Body posture is the basic requirement of aerobics, and it is of great significance to improve the effect of improving the quality of movement and artistic expression.

Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment

In the era of big data, it is aimed to use big data technology to form an effective early warning and prevention of Internet credit. The BP neural network algorithm is applied to determine the number of nodes, activation function, learning rate, and other parameters of each layer of the BP neural network. Also, many data samples are used to build an early warning model of Internet credit risk. The constructed model is trained and tested. Finally, the genetic algorithm (GA) is used to optimize the neural network to improve the accuracy of early warning. The results show that based on 450 data samples from 90 enterprises over five years and the risk interval divided by the “3σ” rule, the Internet credit risk level is initially determined. Then, the neural network is trained and tested. The network output rate is as high as 85%. To avoid the defect of the BP neural network falling into local extreme value, GA is used to optimize the neural network. The warning is more accurate, and the error is smaller. The accuracy rate can reach 97%. Therefore, the use of BP neural network for early warning and assessment of Internet credit risk has good accuracy and computing efficiency, which expands the application of BP neural network in the field of Internet finance, and provides a new development direction for the early warning and assessment of Internet credit risk.

Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

In this paper, a prediction method based on artificial neural network was developed to rapidly determine the residual shear resistance of reinforced concrete (RC) beams after fire. Firstly, the temperature distribution along the beam section was determined through finite element analysis using software ABAQUS. A residual shear strength calculation model was developed and validated using the test data. Using this model, 384 data entries were derived for training and testing. The input layer of neural network involved parameters of beam height, beam width, fire exposure time, cross-sectional area of stirrup, stirrup spacing, concrete strength, and concrete cover thickness. The output was the shear resistance of RC beams. It was found that use of BP neural network could precisely predict the post-fire shear resistance of RC beams. The predicted data were highly consistent with the target data. Thus, this is a novel method for computing post-fire shear resistance of RC beams. Using this new method, further investigation was also made on the effects of different parameters on the shear resistance of the beams.

Settlement Prediction of Buildings Based on BP Neural Network

At present, the traditional neural network model have been used in settlement prediction of buildings area, but there are some limitations. In this paper, BP neural network is applied in the settlement prediction of buildings and the prediction result is compared with the measured values. The results show that: the use of BP neural network to predict the settlement of existing buildings is feasible. The study results can provide a reference for the anti-seismic performance census of existing large area buildings.

Combined use of BP neural network and computational integral imaging reconstruction for optical multiple-image security

Integral imaging can provide a feasible and efficient technique for multiple-image encoding system. The computational integral imaging reconstruction (CIIR) technique reconstructs a set of plane images along the output plane, whereas the resolution of the reconstructed images will degrade due to the partial occlusion of other reconstructed images. Meanwhile, CIIR is a pixel-overlapping reconstruction method, in which the superimposition causes the undesirable interference. To overcome these problems, we first utilize the block matching algorithm to eliminate the occlusion-disturbance and introduce the back-propagation neural network algorithm to compensate for the low-resolution image. In the encryption, a computational integral imaging pickup technique is employed to record the multiple-image simultaneously to form an elemental image array (EIA). The EIA is then encrypted by combining the use of maximum length cellular automata (CA) and the double random phase encoding algorithm. Some numerical simulations have been made to demonstrate the performance of this encryption algorithm.

The Use of BP Neural Network in the Landslide Prediction of Three Gorges Reservoir

Landslide prediction was one of the important tasks in the monitoring and early warning of the Three Gorges reservoir. On the basic of the BP neural network which was combined with the algorithm model, we took the typical Baishuihe landslide in Three Gorges reservoir for example. With the use of GPS monitoring data in surface displacement and its predisposing factors which was reservoir level, rainfall and so on, we built BP neural network prediction model. The monitoring of data recent years was used in the training and validation of the network .The results show that the BP neural network prediction model prediction can be used in the prediction of landslide deformation and the accuracy is high.

The Analysis of Demand Characteristics of Passenger Transportation Based on BP Neural Network

To quantitatively analyze travelers preference under different influence factors on transport mode, the thoughts of orthogonal design is introduced into optimizing questionnaires without the loss of information. Combined with stated preference survey method, data related to travelers preference are collected. In order to analyze survey data scientifically and roundly, BP neural network is used. By taking non-investigated options as inputs, BP neural network can figure out the scores of all options which are not be surveyed. At last a numeric example demonstrates the use of BP neural network in the analysis of demand characteristic of passenger transposition is valid and feasible.

Forecasting the Exchange Rate Based on BP Neural Network in Combination with Simulated Annealing Algorithm

In this study, the author focus on the exchange rate forecasting. Exchange rates fluctuation is extremely complex, not only contains the linear part but also includes non-linear elements, In this paper, Simulated Annealing Algorithm is introduced to overcome the neural network easy fall into local minimum defects in BP neural network basis, in order to optimize the network weights and thresholds, and thus improve the prediction accuracy. Through several forecast experiments about the major currencies against, the result show that compare to the single use of BP neural network, after introduced Simulated Annealing Algorithm, the prediction accuracy and stability has been further improved, meanwhile time-consuming less than genetic algorithms and other optimization algorithms.

Noninvasive measurement of the human RBC concentration based on BP NN model

In the present article, the BP neural network's arithmetic model is applied to the noninvasive detection of the concentration of the red blood cell of human body. Due to the use of BP neural network in the modeling and analysis of the dynamic spectrum data and the actual measured value of the red blood cell, the authors get a better result which refers to that the output value tracks the expected result very well. The related coefficient R can reach 0.993. When predicting the output value in the way of the BP neural network model, the maximal relative error is only 4.7%, average relative error is 2.1%, so the authors can say that it has more ideal prediction ability. The experimental result shows that the BP neural network model can be accurate in dealing with the nonlinear relation between the dynamic spectrum data and human erythrocyte practical value and it can make the method of noninvasive blood analysis more useful in clinical application. So it has a high application value.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

Combined Forecasting for Short-Term Output Power of Wind Farm

Wind power forecast is of great significance for power grid operation and scheduling. The effection of historical time series of output power and weather factors to wind power are considered in this paper. By use of BP neural network, an iterative forecasting model about output power time series is built. An Elman neural network forecasting model is established between numerical weather prediction data and output power. Then combining the above two forecasting models using covariance optimal combination method, a combined forecasting model for wind power is achieved so as to use all effective information of different data. The simulation experiment shows that the prediction accuracy has been improved by the combination forecast.

Research on Pattern Recognition Based on BP Neural Network

BP neural network has strong fault-tolerant and adaptive learning capacity, so it is widely used in pattern recognition. Based on the classic BP neural network, parameters of the BP algorithm has been optimized, which achieved a classification based on the improved BP neural network algorithm. By discussing the use of BP neural network in the application of pattern classification recognition, this paper detailedly studies the recognition effect of various parameters. Experimental results show that the improved algorithms has very good practical value.

Research on License Plate Recognition System Based on Computer Vision

In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for program transfer and results display. The experimental results showed that the location of license plate was exact and the character recognition rate was high.

Use of BP Neural Network in Near-Infrared Spectroscopy Calibrations for Predicting of Wood Density

Application of BP neural network and NIRS for larch wood density prediction was investigated in this paper. The original spectra were collected and pretreated with the first derivative and 9 point smoothing. Eleven typical wave lengths were selected as BP network inputs to establish prediction model for wood density. Model was validated using cross-validation approach. The prediction correlation coefficient (R) is 0.916 while the root mean square error of prediction (RMSEP) is 0.0221. The results showed that using BP neural network in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

The rapidly solidified aging copper alloy by BP neural network

Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu−Cr−Zr copper alloy. The use of BP neural network (NN) is presented to model the non-linear relationship between parameters of age hardening processes and the mechanical and electrical properties of rapdily solidified Cu−Cr−Zr alloy. The improved model is developed by the Levenberg-Marquardt training algorithm and the good generalization performance is demonstrated. So, an important foundation has been laid for optimisticaly controlling the rapidly solidified aging processes of Cu−Cr−Zr alloy.