Application Of BP Neural Network Research Articles

基于主成分分析的BP神经网络在水华预测中的应用

基于主成分分析的BP神经网络在水华预测中的应用

The Application of BP Neural Network in Leukocyte Classification Recognition

Artificial neural network is the most widely used and most mature technology in artificial intelligence information fusion; classification recognition is one of the main application. In this article, the 6-13-6 three layer BP neural network classifier is designed for white blood cell image, and the BP network is trained and simulated by using the feature data extracted from the white blood cell image. The simulation results show that the classifier can classify the cells rapidly and accurately.

Relative Pose of IMU-Camera Calibration Based on BP Network

There are many applications of the combination of IMU (Inertial Measurements Unit) and camera in fields of electronic image stabilization, enhancement reality and navigation where camera-IMU relative pose calibration is one of the key technologies, which may effectively avoid the cases of insufficient feature points, unclear texture, blurred image, etc. In this paper, a new camera-IMU relative pose calibration method is proposed by establishing a BP neural network model. Thus we can obtain the transform from IMU inertial measurements to images and achieve camera-IMU relative pose calibration. The advantage of our method is the application of BP neural network using Levenberg-Marquardt algorithm, avoiding more complex calculations for the whole process. And it is convient for the application of camera-IMU combination system. Meanwhile, nonlinearities and noises are compensated while training and the impact of gravity can be ignored. Our experimental results demonstrated that this method can achieve camera-IMU relative pose calibration and the accuracy of quaternion estimation has reached about 0.01.

Application of BP neural network in acoustic wave measurement system

Acoustic wave measurement technology is the supporting technology in acoustic wave field. It is important to study acoustic wave with high precision and reliability testing equipment and scientific testing methods. The mathematical model of this acoustic wave measurement system was analyzed on the building of the system. The BP neural network algorithm was used in order to attain the higher accuracy for the acoustic wave measurement system. Frequency domain calibration was carried out by which the amplitude/frequency character curve of this system could be obtained. Then the model of the system was established by BP neural network algorithm. Finally, the validity of the established model was tested. The conclusion was that the math model reflected the original acoustic wave measurement system’s character through the regression result in the frequency domain.

Application of BP Neural Network in Prediction of Flow Temperature of Ash from Blended Coals

Ash flow temperature is an important indicator of blended coals gasification, determined by its chemical composition. In view of nonlinear relationship between chemical compositions and flow temperatures of ashes from blended coals and individual coals, BP neural networks were built to forecast chemical compositions and flow temperatures of ashes. It was shown that relative errors for predicting contents of oxides, such as silicon oxide, alumina, iron oxide, calcium oxide, magnesium oxide, potassium oxide and sodium oxide, in ash from blended coals with BP neural network range from 0.01% to 4.14%, relative errors for predicting flow temperature of ash from blended coals by BP neural network based on prediction results of ash chemical composition locate in range of 0.20%-3.48%, illustrating good agreements with the measured flow temperatures. BP neural network can offer a shortcut for coal blending technology.

Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment

BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

The Application of BP Neural Network Optimized by Genetic Algorithm in Students' Comprehensive Quality Evaluation

The Application of BP Neural Network Optimized by Genetic Algorithm in Students' Comprehensive Quality Evaluation

A Novel Application of BP Neural Networks to Evaluate the Safety of Power Grid in Wildfire Disasters

A Novel Application of BP Neural Networks to Evaluate the Safety of Power Grid in Wildfire Disasters

BP Neural Network Application Model of Predicting the Apple Hardness

BP Neural Network Application Model of Predicting the Apple Hardness

基于纹理特征的图像复杂度研究

In order to better describe the internal complexity of image,the establishment of the mathematic model between the image complexity and each index is the key step to study the complexity of image. Firstly,starting from the image texture,we try to establish a quantitative and precise mathematical description of the relationship between the image and the complexity of various indicators. There is no clear mathematical relationship between the image complexity and the measurable indicators,so gray level co-occurrence matrix( GLCM) is used to analyze the main characteristic parameters of the texture. The image complexity evaluationmethod is proposed based on BP neural network. Then a nonlinear mathematical evaluation model between image complexity and each index is established. And the weight values and index are obtained by the training for the neural network and learning through numbers of pictures. The verification results show that the evaluation model is able to reflect the internal complexity of the image truly,and the experimental results obtained are consistent with human visual perception. It is of a certain reference value for the application of BP neural network to study the image complexity.

Application of BP neural network in DNBR prediction

Application of BP neural network in DNBR prediction

The Establishment and Application of BP Neural Network Combined Forecasting Model

The Establishment and Application of BP Neural Network Combined Forecasting Model

Multi-Sensor Data Fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis

In this paper, for the frequent faults problems of the mine air compressor main motor, we use the BP neural network learning algorithms on the basis of the theory of multi-sensor data fusion. The collected characteristic signals were processed by the method of data fusion, and we could get the current motor fault state value. Compared to the experimental results, it can realize the fault diagnosis of mine equipment obviously.

Financial Time Series Prediction Based on BP Neural Network

BP neural network is promising methods for the prediction of financial time series because it use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies BP neural network to predicting the stock price index. In addition, this study examines the feasibility of applying BP neural network in financial forecasting. The experimental results show that BP neural network provides a promising alternative to stock market prediction.

Application of BP Neural Network in Products Forecast

BP neural network has parallel processing capabilities and a good approximation of the nonlinear mapping and gradually been widely used in the forecast. Because it is difficult to determine the BP neural network structural model, this paper presents the design ideas from six aspects. Combined with the practical example-mushroom classification, this paper presents the affect of the hidden layer, learning rate, training function on BP network and has some practical significance.

Application of BP Neural Network in PSD Nonlinear Correction

Because resistance of two-dimensional position sensitive detector's (PSD) photo surface is not absolute uniformity that its output is nonlinear. It is this feature enables the PSD difficult to measure small displacement. In order to solve this problem, BP neural network is proposed to solve the problem of PSD nonlinear correction after the study of traditional nonlinear correction method; BP neural network would have a strong ability of nonlinear mapping after training, and it can approach arbitrarily contact function by arbitrary precision, and MATLAB neural networking boxes can simulate BP neural network easily. Simulation and verification indicate that the method has a remarkable effect in solving nonlinear problems, and it can meet system requirements.

The Application of BP Neural Networks in Cable-Stayed Construction Control

Through the related technical information of the monitoring of bridge construction, this is to determine BP neural network's input and output parameters, to establish the neural network fore-casted model, and to carry through the prediction to the main beam of deflection. In the application, the data of deflection deviation has generated through the cast-in-place segmental cantilever to train the BP neural network. The deviations which are produced in the pre-run neural networks predict the follow-up segment in construction, and then simulation control in the cable-stayed bridge is realized in construction process.

Research on the Application of BP Neural Networks in 3D Reconstruction Noise Filter

At present, in the most of the digital image system, the input image is used to freeze the multi-dimensional image scanning way again into a one dimensional signal, then carries on the processing, storage, transmission and processing. Finally tend to form multi-dimensional image signal, and the image noise will also be decomposed and compounded. Electrical systems and outside influences in these procedures will enable precise analysis of complexity of image noise. According to off-line learning method of neural networks, this paper focus on the noise filter in the 3Dreconstruction process in order to make the image clearer.

High-Speed Machining Time Model Prediction of Combination Framework Based on BP Neutral Network

The process parameter prediction and analysis of high-speed NC machining of cast aluminum combination framework is one of the important research directions of mechanical processing. In order to make sure predicting the high-speed NC machining parameters of cast aluminum framework while machining、improve production efficiency、reduce the requirement for machine tool accuracy and technology level of operating personnel, established high-speed NC machining system time forecast model of combination framework applying BP neural network, doing corresponding verification of high-speed NC machining process parameters, then designed a set of process suitable for cast aluminum combination framework high-speed NC machining, providing feasible solution for high precision machining.

Design of a Novel Variable Bit Rate Video Traffic Prediction Model Based on Neural Networks

The video business has gradually become the main business of the network traffic, video traffic is Variable Bit Rate (VBR), which has nonlinear and sudden features, a single prediction model is not fit for describing those features, and prediction accuracy is not high. Because of wavelet analysis has advantages of multi-resolution and dealing with unexpectedness, and the neural network has better nonlinear fitting characteristics, in order to improve the prediction performance, the paper researched on the problem of VBR video traffic prediction based on neural networks, a novel prediction model AMWM is proposed, the model firstly introducing BP neural network to multi-fractal wavelet model, and designing prediction method, which introduced multi-fractal wavelet model to model VBR video traffic, and then applying BP neural network to forecast scale coefficient decomposed, and forecast multiplier using AR model, and predict traffic generated by wavelet reconstruction. Finally, the model is built and simulated. The experimental result shows that prediction performance based AMWM is better compared with multi-fractal wavelet model.