scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Research on Prediction Model of Car Sales Based on Grey Neural Network

2014 ◽  
Vol 889-890 ◽  
pp. 1569-1573
Author(s):  
Shou Qi Cao ◽  
Ya Wen Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Small Sample ◽  
Just In Time ◽  
Practical Application ◽  
Correlation Data ◽  
Sample Data ◽  
Management Technology ◽  
Car Sales ◽  
Zero Inventory

Nowadays, zero inventory and just in time production and management technology are got attention by more and more domestic and foreign automobile enterprises. Accurate prediction of car sales is the key technology for ensuring zero inventory and product planning of enterprises. In this paper, a prediction model about car sales based on grey neural network was researched. Combining the advantages of grey model in the way of dealing of small sample data, and BP neural network in finding regularity of weak correlation data, the accuracy of the prediction results was improved. Finally, the model was validated by practical application of automobile enterprise production data.

A numerical external pitting damage prediction method of buried pipelines

2020 ◽  
Vol 38 (5) ◽  
pp. 433-444
Author(s):  
Eliceo Sosa ◽  
Adrian Verdín Martinez ◽  
Jorge L. Alamilla ◽  
Antonio Contreras ◽  
Luis M. Quej ◽  
...  
Keyword(s):  
Neural Network ◽  
Corrosion Rate ◽  
Prediction Model ◽  
Influencing Factors ◽  
Prediction Models ◽  
Prediction Method ◽  
Corrosion Damage ◽  
Buried Pipelines ◽  
Damage Prediction ◽  
External Corrosion

AbstractThe work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

scholarly journals A Link Prediction Method Based on Neural Networks

2021 ◽  
Vol 11 (11) ◽  
pp. 5186
Author(s):  
Keping Li ◽  
Shuang Gu ◽  
Dongyang Yan
Keyword(s):  
Neural Network ◽  
Link Prediction ◽  
Prediction Models ◽  
Prediction Method ◽  
Network Evolution ◽  
Global Network ◽  
Network Efficiency ◽  
Structure Reliability ◽  
The Neural Network ◽  
Network Method

Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

scholarly journals Mango Grading System Based on Optimized Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Zheng ◽  
Tao Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Small Sample ◽  
Parameters Optimization ◽  
Average Error ◽  
Grading System ◽  
Data Set ◽  
Operation Speed ◽  
Sample Data

In order to achieve the accuracy of mango grading, a mango grading system was designed by using the deep learning method. The system mainly includes CCD camera image acquisition, image preprocessing, model training, and model evaluation. Aiming at the traditional deep learning, neural network training needs a large number of sample data sets; a convolutional neural network is proposed to realize the efficient grading of mangoes through the continuous adjustment and optimization of super-parameters and batch size. The ultra-lightweight SqueezeNet related algorithm is introduced. Compared with AlexNet and other related algorithms with the same accuracy level, it has the advantages of small model scale and fast operation speed. The experimental results show that the convolutional neural network model after super-parameters optimization and adjustment has excellent effect on deep learning image processing of small sample data set. Two hundred thirty-four Jinhuang mangoes of Panzhihua were picked in the natural environment and tested. The analysis results can meet the requirements of the agricultural industry standard of the People’s Republic of China—mango and mango grade specification. At the same time, the average accuracy rate was 97.37%, the average error rate was 2.63%, and the average loss value of the model was 0.44. The processing time of an original image with a resolution of 500 × 374 was only 2.57 milliseconds. This method has important theoretical and application value and can provide a powerful means for mango automatic grading.

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 A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kun Zhang ◽  
Zhao Hu ◽  
Xiao-Ting Gan ◽  
Jian-Bo Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Traffic ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fuzzy Wavelet Neural Network

Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.

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