Research about Dynamic Performance Prediction for Layout Structure of Globoidal Cam Machine

2014 ◽  
Vol 607 ◽  
pp. 321-324
Author(s):  
Yi Yong Yao ◽  
Li Ping Zhao ◽  
Guang Zhou Diao ◽  
Hu Zhao ◽  
Pen Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Dynamic Performance ◽  
Prediction Method ◽  
Structure Design ◽  
Machine Layout ◽  
Layout Structure ◽  
And Performance

Aiming to the layout structure design and performance prediction for globoidal cam machine, a dynamic performance prediction method for machine layout structure is proposed in this paper. With the method, the motion transmission and layout structure are determined based on the mapping rules between function and structure. The prediction model for dynamic performance is established based on BP neural network, which is used to optimize the dynamic performance of layout structure for globoibal cam machine.

scholarly journals Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinjuan Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Training Model ◽  
Sports Performance ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Optimization of Fiber Laser Welding Process Viarables and Performance Prediction Based on BP Neural Network

Applied laser ◽  
2010 ◽  
Vol 30 (6) ◽  
pp. 479-482
Author(s):  
郭亮 Guo Liang ◽  
王少华 Wang Shaohua ◽  
张庆茂 Zhang Qingmao ◽  
徐鹏嵩 Xu Pengsong ◽  
庞振华 Pang Zhenhua
Keyword(s):  
Neural Network ◽  
Laser Welding ◽  
Fiber Laser ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Welding Process ◽  
Fiber Laser Welding ◽  
And Performance ◽  
Laser Welding Process

scholarly journals Forecast of Football League Sales Effect Based on BP Neural Network

2020 ◽  
Author(s):  
Zhubo Xu ◽  
Weifeng Qin
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Percentage Difference ◽  
The Neural Network ◽  
Sample Data ◽  
Real Value ◽  
Ancient Times ◽  
The Difference

Abstract Football is one of the sports that is loved by people all over the world. Its sales ability in the league should not be underestimated. Moreover, football has been developed in our country since ancient times and has a huge fan base, and fans are the main target of football league sales. Predicting the sales effect of the football league is helpful for the seller to formulate a suitable sales strategy and avoid the problem of product surplus. This article mainly introduces the prediction research of football league sales effect based on BP neural network, and intends to provide ideas and methods for predicting the sales effect of football league. This paper puts forward the basic method of the sales effect prediction of the football league and the BP neural network football league sales effect prediction method to analyze and predict the sales effect of the football league. In addition, the steps of establishing BP neural network design, building BP neural network football league sales effect prediction model and applying BP neural network football league sales effect prediction model are also proposed. The experimental results of this article show that the difference between the fitting part of the neural network model and the real value of the football league sales effect sample data is in the range of , the error percentage difference is small, and the prediction results are valid。

scholarly journals Bayesian regularized NAR neural network based short-term prediction method of water consumption

2019 ◽  
Vol 118 ◽  
pp. 03024
Author(s):  
Jianyu Liu ◽  
Linxue Zhao ◽  
Yanlong Mao
Keyword(s):  
Neural Network ◽  
Water Supply ◽  
Prediction Model ◽  
Water Consumption ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Short Term ◽  
Neural Network Prediction ◽  
Short Term Prediction ◽  
Consumption Prediction

With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of water supply scheduling, and its accuracy also directly affects the effectiveness of water supply scheduling. This paper analyzes the regularity of water consumption time series, establishes a short-term water consumption prediction model based on Bayesian regularized NAR neural network, and compares and evaluates the prediction effect of the model. The verification results show that the Bayesian based NAR neural network prediction model has higher adaptability to the water consumption prediction than the standard BP neural network and the Bayesian regularized BP neural network. The prediction accuracy can more accurately reflect the short-term variation of water consumption.

scholarly journals Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2709 ◽  
Author(s):  
Han ◽  
Nan ◽  
Su ◽  
Chen ◽  
Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Centrifugal Pump ◽  
Relative Error ◽  
Neural Network Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Pump Performance ◽  
Hidden Layer ◽  
Bp Neural Network Model

With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.

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