Grey prediction models for the standard limit of vehicle noise

2017 ◽  
Vol 232 (7) ◽  
pp. 973-979 ◽  
Author(s):  
Zhendong Zhao ◽  
Changzheng Hu
Keyword(s):  
Prediction Models ◽  
System Development ◽  
Original Data ◽  
Grey Theory ◽  
Grey Prediction ◽  
Vehicle Noise ◽  
Direct Model ◽  
The Future ◽  
Quadratic Fitting ◽  
Verhulst Model

With an increasing number of vehicles and increasing environmental protection requirements, countries have accelerated the rate of revision of automobile noise standards and legislation. Scientific prediction of the limiting values in future noise standards is helpful to promote the development of automobile noise reduction technology and measurement analysis technology. The development of noise standard limits has its own objective laws and is restricted to the current and future developments in automotive technology. The amplitude of noise will be reduced increasingly less in the future. Grey prediction theory can explore the variation rules by processing a few effective data. In this paper, grey theory is used to deal with the limited original data in the vehicle noise standard. Non-equal-interval quadratic fitting of the grey Verhulst direct model to predict the future noise standard limits is selected on the basis of calculation and comparison of different models. The Verhulst model is employed to describe the system development by using the characteristics of saturation. By means of quadratic fitting, the accuracy of the Verhulst model can be further improved. The simulation results show the validity and the accuracy of the model. The prediction result is useful for standards and regulations makers and for car manufacturers.

Applying Grey Forecasting Model on the Milk Yield of Guangxi

2014 ◽  
Vol 556-562 ◽  
pp. 4461-4464
Author(s):  
De Wang Li ◽  
Da Ming Xu ◽  
Wu Sheng Wang
Keyword(s):  
Milk Production ◽  
Prediction Models ◽  
Production Data ◽  
Forecasting Model ◽  
Grey Theory ◽  
Market Demand ◽  
Grey Prediction ◽  
Grey Forecasting ◽  
Livestock Products ◽  
Statistical Yearbook

By using milk production data of Guangxi statistical yearbook from 2001 to 2010, based on the Grey theory and Grey prediction models, which are GM(1,1), have been adopted to predict the milk throughput of Guangxi in this paper. So we establish the GM(1,1) prediction model and predict the milk production of Guangxi from 2011 to 2020. The results show that the Grey theory and Grey prediction models have good simulation and feasibility. At the same time, with the combination of livestock products market demand, we provide livestock enterprises and farmers with some appropriate recommendations.

scholarly journals A Novel Grey Power-markov Model for the Prediction of China's Electricity Consumption

2021 ◽  
Author(s):  
Liqin Sun ◽  
Youlong Yang ◽  
Tong Ning ◽  
Jiadi Zhu
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Original Data ◽  
Development Trend ◽  
Statistical Hypothesis ◽  
Grey Prediction ◽  
New Information ◽  
Proposed Model ◽  
Grey Models

Abstract The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

Development prediction of logistics industry in Henan province and its dynamic analysis

2014 ◽  
Vol 4 (2) ◽  
pp. 186-194 ◽  
Author(s):  
Yimin Huang
Keyword(s):  
Future Development ◽  
Regional Economy ◽  
Prediction Models ◽  
Development Trend ◽  
Henan Province ◽  
Grey Prediction ◽  
Logistics Industry ◽  
Content Type ◽  
The Future ◽  
Freight Volume

Purpose – The purpose of this paper is to establish a group of grey prediction models and relative degree model to study the characteristics and trend of the logistics industry development in Henan province scientifically. The study results can provide references for the development policy of the logistics industry in Henan province. Design/methodology/approach – The paper constructs grey prediction models and grey buffer operator models which are related to the distribution of logistics industry in Henan province, and selects prediction models by comparing model accuracy, and use them to forecast the development trend of logistics industry in future ten years of Henan province. Using the grey relative models, the paper analyses development dynamic and prospect which support the development of logistics industry, and provide some references for transferring the pattern of economic growth of Henan province, forming new economic growth point and formulating relevant policies. High prediction accuracy models are selected to forecast the future development trend of logistics industry in the next ten years. Findings – Results show that the modern logistics industry in Henan province has been a steady growth in overall, the main growth points of the logistics industry development in Henan province are roadway miles (km), roadway (100 million tonnes/km), freight turnover (100 million tonnes/km) and waterway (100 million tonnes), the growth points for the future development of logistics industry in Henan province are the roadway freight volume, roadway passenger volume and waterway freight volume. Practical implications – Regional economic competition has become an important index for measuring a country's economic development level. Logistics industry plays an important role in the regional economic development, such as promoting coordinated development of regional economy and upgrading industrial optimization, and playing a major role in industrial transfer. Hence, logistics industry, which is urgently needed to solve by the government, has become important forces for promoting the growth of economy and a basic pillar industries of regional economy. Originality/value – The paper presents the systematic results of development prediction of modern logistics industry in Henan province and its dynamic analysis by using grey systems theory, not only to predict the trend of the development of the logistics industry, also to analyse the future development of logistics industry in the leading power factors.

Prediction Modeling Method of Interval Grey Number Based on Different Type Whitenization Weight Functions

2013 ◽  
Vol 411-414 ◽  
pp. 2074-2080 ◽  
Author(s):  
Lian Ming Zhao ◽  
Bo Zeng
Keyword(s):  
Prediction Model ◽  
Weight Function ◽  
Prediction Models ◽  
Weight Functions ◽  
Grey Theory ◽  
Grey Prediction ◽  
Grey Models ◽  
Different Types ◽  
Number Sequences ◽  
Grey Number

The modeling objects of existing prediction models for interval grey number are limited to the interval grey number sequences with unknown or the same type of whitenization weight function. Therefore, the existing methods are useless when the types of whitenization weight function of interval grey number in the modeling sequence are heterogeneous. On the basis of the existing prediction models for interval grey number and according to the axiom of undecreased degree of greyness and grey number, the present paper build a prediction model for interval grey number based on different types of whitenization weight functions through expanding the calculation of "kernel and grey degree" of the interval grey number. At last, this model was applied in forecasting the demand for emergency materials in disaster. The research results are significant for enriching and perfecting the grey prediction model theory system, and extending the applied scope of grey models and promoting the effective association of the grey theory and the practical issues.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

scholarly journals Forecasting Crude Oil Consumption in China Using a Grey Prediction Model with an Optimal Fractional-Order Accumulating Operator

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Huiming Duan ◽  
Guang Rong Lei ◽  
Kailiang Shao
Keyword(s):  
Particle Swarm Optimization ◽  
Crude Oil ◽  
Fractional Order ◽  
Gamma Function ◽  
Particle Swarm ◽  
Grey Theory ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Swarm Optimization ◽  
Production And Consumption

Crude oil, which is an important part of energy consumption, can drive or hinder economic development based on its production and consumption. Reasonable predictions of crude oil consumption in China are meaningful. In this paper, we study the grey-extended SIGM model, which is directly estimated with differential equations. This model has high simulation and prediction accuracies and is one of the important models in grey theory. However, to achieve the desired modeling effect, the raw data must conform to a class ratio check. Unfortunately, the characteristics of the Chinese crude oil consumption data are not suitable for SIGM modeling. Therefore, in this paper, we use a least squares estimation to study the parametric operation properties of the SIGM model, and the gamma function is used to extend the integer order accumulation sequence to the fractional-order accumulation generation sequence. The first-order SIGM model is extended to the fractional-order FSIGM model. According to the particle swarm optimization (PSO) mechanism and the properties of the gamma function of the fractional-order cumulative generation operator, the optimal fractional-order particle swarm optimization algorithm of the FSIGM model is obtained. Finally, the data concerning China’s crude oil consumption from 2002 to 2014 are used as experimental data. The results are better than those of the classical grey GM, DGM, and NDGM models as well as those of the grey-extended SIGM model. At the same time, according to the FSIGM model, this paper predicts China’s crude oil consumption for 2015–2020.

scholarly journals Prizes Signal Scientific Revolutions

2020 ◽  
Author(s):  
Ching Jin ◽  
Yifang Ma ◽  
Brian Uzzi
Keyword(s):  
Original Data ◽  
Difference In Differences ◽  
Prize Money ◽  
Scientific Revolutions ◽  
Technological Advances ◽  
Funding Agencies ◽  
The Future ◽  
Star Scientists ◽  
Quality Of Research

Abstract Scientific revolutions affect funding, investments, and technological advances, yet predicting their onset and projected size and impact remains a puzzle. We investigated a possible signal predicting a topic’s revolutionary growth – its association with a scientific prize. Our analysis used original data on nearly all recognized prizes associated with 11,539 scientific topics awarded between 1960 and 2017 to examine the link between prizes and a topic’s unexpected growth in productivity, impact, and talent. Using difference-in-differences regressions and counterfactuals of matched prizewinning and non-prizewinning topics, we found that in the year following the receipt of a prize, a topic experiences an onset of extraordinary growth in impact and talent that continues into the future. At between five to 10 years after the prize year, prizewinning topics are 38% more productive and 31% more impactful in citations, retain 53% more incumbents, and gain 35% more new entrants and 46% more star scientists than their non-prizewinning peer topics. While prizewinning topics grow unexpectedly fast in talent and impact, funding does not drive growth; rather, growth is positively associated with the recency of work on the topic, discipline-specific rather than general awards, and prize money. These findings advance understanding of scientific revolutions and identify variations in prize characteristics that predict the timing and size of a topic’s revolutionary growth. We discuss the implications of these findings on how funding agencies and universities make investments and scientists commit time and resources to one topic versus another, as well as on the quality of research.

scholarly journals Does Climate Change Affect the Yield of the Top Three Cereals and Food Security in the World?

Earth ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 45-71
Author(s):  
Dhurba Neupane ◽  
Pramila Adhikari ◽  
Dwarika Bhattarai ◽  
Birendra Rana ◽  
Zeeshan Ahmed ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Greenhouse Gases ◽  
Prediction Models ◽  
Molecular Techniques ◽  
Lower Amount ◽  
Cereal Crops ◽  
The World ◽  
The Future ◽  
World Food Security

Climate prediction models suggest that agricultural productivity will be significantly affected in the future. The expected rise in average global temperature due to the higher release of greenhouse gases (GHGs) into the atmosphere and increased depletion of water resources with enhanced climate variability will be a serious threat to world food security. Moreover, there is an increase in the frequency and severity of long-lasting drought events over 1/3rd of the global landmass and five times increase in water demand deficits during the 21st century. The top three cereals, wheat (Triticum aestivum), maize (Zea mays), and rice (Oryza sativa), are the major and staple food crops of most people across the world. To meet the food demand of the ever-increasing population, which is expected to increase by over 9 billion by 2050, there is a dire need to increase cereal production by approximately 70%. However, we have observed a dramatic decrease in area of fertile and arable land to grow these crops. This trend is likely to increase in the future. Therefore, this review article provides an extensive review on recent and future projected area and production, the growth requirements and greenhouse gas emissions and global warming potential of the top three cereal crops, the effects of climate change on their yields, and the morphological, physiological, biochemical, and hormonal responses of plants to drought. We also discuss the potential strategies to tackle the effects of climate change and increase yields. These strategies include integrated conventional and modern molecular techniques and genomic approach, the implementation of agronomic best management (ABM) practices, and growing climate resilient cereal crops, such as millets. Millets are less resource-intensive crops and release a lower amount of greenhouse gases compared to other cereals. Therefore, millets can be the potential next-generation crops for research to explore the climate-resilient traits and use the information for the improvement of major cereals.

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.

The Application of Grey Prediction on the Fast Control Power Supply in EAST

2013 ◽  
Vol 427-429 ◽  
pp. 1739-1742
Author(s):  
Hai Hong Huang ◽  
Jia Miao ◽  
Hai Xin Wang ◽  
Feng Feng Wang
Keyword(s):  
Input Signal ◽  
Power Supply ◽  
Grey Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Control Power ◽  
Input Signals ◽  
Simulation And Experiment ◽  
One Step ◽  
Fast Control

Based on the grey theory, a novel model is built to predict the input signal of fast control power supply used in Experimental Advanced Superconducting Tokamak (EAST). The model can be used as online metabolic grey filtering and one-step prediction of different input signals. Results of simulation and experiment show that the predicting algorithm based on the grey system model can predict the input signal primarily.

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