Grey Prediction Algorithm Research Articles

Exploration of Optimisation Mechanism of Innovation and Entrepreneurship and Student Management for College Students Driven by Big Data

Abstract The current employment situation is getting tighter and tighter, and innovation and entrepreneurship of college students have gradually become the focus of attention of the government and colleges and universities. This paper preprocesses the student data, constructs the data matrix using the gray prediction algorithm, uses the least squares method, establishes the differential equation solution, and obtains employment prediction data. For the employment prediction can be known to the innovation and entrepreneurship situation of college students, this paper further introduces the Logistic regression analysis, establishes a multivariate regression analysis algorithm based on the influencing factors of innovation and entrepreneurship, and provides theoretical support for the study of the influencing factors and the optimization mechanism of the management of innovation and entrepreneurship student management. This paper takes University H in Zhejiang Province of China as the research site to explore the current situation of innovation and entrepreneurship of its college students in depth. The students who choose self-help entrepreneurship in 2022-2023 only accounted for 2.42% and 2.04%, and the predicted percentage in 2024 is 1.93%, so the current situation of innovation and entrepreneurship is dismal. In the analysis of college students’ innovation and entrepreneurship influencing factors, the factors of specialty, personal work experience, and buddy entrepreneurship experience all showed significant differences (P<0.05). In the regression analysis of entrepreneurship education and entrepreneurship policy on entrepreneurial intention, the regression coefficients of entrepreneurship education and entrepreneurship policy are 0.276 and 0.136, respectively, which both reach the level of significance, and all of them are in a significant positive proportional relationship.

Retracted: A Grey Prediction Algorithm for Enterprise Human Resource Management Validity Model Construction and Improvement

Retracted: A Grey Prediction Algorithm for Enterprise Human Resource Management Validity Model Construction and Improvement

A mathematical model for the spread of Omicron virus based on grey prediction algorithm

After two years of the Newcastle epidemic, the emergence of a mutant strain of Omicron virus has once again raised a high level of alarm worldwide, and it has rapidly replaced the Delta strain as the major strain in the world epidemic with high infectivity. In order to study the spread of the epidemic in a qualitative and quantitative manner, this paper uses relevant mathematical modeling ideas and methods, combined with the background of big data, to conduct relevant research and analysis in order to provide guidance for the actual fight against the epidemic and facilitate scientific research and response measures. In this paper, a gray prediction algorithm is used to predict the future epidemic data. A metabolic algorithm is introduced to recursively correct the prediction residuals and build a multilevel gray model. In this paper, to investigate the role of vaccines on epidemics, the R values under each dose of vaccine were calculated by selecting data from several countries and regions using fitted curve idealization. The prediction of the data yields the conclusion that vaccination is indeed directly effective in controlling the spread of the epidemic.

The recursive grey model and its application

The fixed structure parameters limit the performance of grey prediction algorithm in unsmoothed time series prediction tasks. Based on the mechanism of recursive iteration, this study proposes a recursive grey model to predict the future trend of renewable energy generation in China. The proposed method has the following improvements: the first is the priority of new information. By introducing a memory factor parameter into the objective function, recursive grey model gives more influence to new observations. The second is dynamic updating scheme of structure parameters and improved iteration base values. The proposed model uses recursive method to solve the structure parameters of each observation, and the new parameters can inherit the previous experience. The third is stable forecasting performance. Based on the principle of minimizing the prediction error of in-sample data, the memory factor optimization method is given. Without additional verification set, recursive grey model can synchronize training and test of in-sample data to avoid over fitting. Finally, the proposed model is applied to predict China's renewable energy generation. The forecast results show that in the next few years, China's hydropower, wind power and solar power generation will grow at an average annual growth rate of 2.6%, 20.7% and 24.9%, respectively. It can be predicted that China's hydropower generation, wind power generation and solar power generation will reach 1475 billion kWh, 3094 billion kWh and 1352 billion kWh, respectively in 2030.

A new grey intelligent prediction algorithm with multiobjective correction strategy

As a combination of the differential equation prediction model and intelligent optimization algorithm, the grey intelligent prediction algorithm has attracted increasing attention due to its outstanding performance in small-sample environments. However, most studies focus only on the improvement of algorithm performance, with little regard to the uniformity, ill-condition and overfitting of the algorithm. To promote the development of this field, we develop a new grey intelligent prediction algorithm with multiobjective correction strategy based on the new weakened accumulation grey optimization model and the multiobjective grey wolf optimizer. In this new prediction algorithm, the new weakened accumulation operation is utilized to enhance the predictive ability of the algorithm and mitigate the ill-condition of the system, the Bernoulli parameter and a discretization technique are used to activate the uniformity and unbiasedness of the algorithm, respectively, and the multiobjective grey wolf optimizer is employed to alleviate the overfitting of the system. Compared with the previous grey intelligent prediction models, the new prediction algorithm is more perfect and reasonable. Taking two energy datasets as research cases, the evaluation results of a system consisting of six evaluation metrics and the Diebold-Mariano test show that the proposed model outperforms the other six comparative models in terms of prediction performance and stability, which confirms the feasibility and validity of the algorithm.

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.

Research on crude oil price forecasting based on computational intelligence

<abstract><p>The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.</p></abstract>

Analysis of fire rescue problems and fire station siting based on fitting algorithms

This paper provides an in-depth study of the fire rescue problem. Based on the number of calls to police at the site in previous years, monthly calls to police in 2022 were predicted through polynomial fitting, cubic spline interpolation, and a two-tone spline interpolation model to analyze and determine the best analytical model. The level of demand for fire stations at the site was measured by using the population density of the site and the number of emergencies per unit time, plus the distance to the nearest fire station at the site to measure its risk level, and finally the cost of building the fire station was considered to measure its composite index, and the site of the fire station was determined based on the composite index. And the grey prediction algorithm as well as the Topsis evaluation algorithm predicts the composite evaluation index for the next 9 years in order to determine where to build the fire station. This study will enable more efficient and uniform fire rescue and more rapid rescue operations to maximize the efficiency of rescue. The relevant data to reasonably predict the various types of emergencies that may occur in the future around the world is an important reference in the issue of fire station location.

Research on macroeconomic Forecasting model based on grey theory

In the modern economic innovation and development, the grey theory and BP neural network algorithm are used to build a new macroeconomic prediction model. On the one hand, the input data can be comprehensively preprocessed, on the other hand, the improved BP neural network algorithm can be used to predict the final results. This paper studies the feasibility and effectiveness of the new macroeconomic forecasting model based on the grey forecasting principle and BP neural network algorithm. The final results show that both grey prediction and BP algorithm can help the staff to predict and analyze the macro economy as soon as possible, so as to grasp more valuable data information and put forward targeted management decisions.

Research on macroeconomic Forecasting model based on grey theory

In the modern economic innovation and development, the grey theory and BP neural network algorithm are used to build a new macroeconomic prediction model. On the one hand, the input data can be comprehensively preprocessed, on the other hand, the improved BP neural network algorithm can be used to predict the final results. This paper studies the feasibility and effectiveness of the new macroeconomic forecasting model based on the grey forecasting principle and BP neural network algorithm. The final results show that both grey prediction and BP algorithm can help the staff to predict and analyze the macro economy as soon as possible, so as to grasp more valuable data information and put forward targeted management decisions.

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

In recent years, with the continuous improvement of system networking, the use of robot electric power equipment is becoming more and more extensive, so it is very necessary to diagnose and monitor its status. Therefore, a diagnosis and monitoring system based on the data mining algorithm is constructed. This study mainly uses the gray prediction algorithm and discrete prediction algorithm; the results showed that after the combination of gray prediction has a certain degree of increase, through the analysis, we can draw the reason for this is that gray prediction algorithm to check the failure data is no longer as input data in the detection of outliers, thereby reducing the noise of the data set, so that the perfomance of oulier detection algorithm can ahieve a great progress. In terms of the running time of outlier detection, the running time of outlier detection using K-Mean algorithm and DBSCAN algorithm increases to a certain extent because the algorithm combines gray prediction and increases the algorithm process.

Prediction and Influencing Factors of College Students' Career Planning Based on Big Data Mining

College students' career planning refers to the process of establishing their own career development goals based on the analysis of their own subjective and objective factors, searching for a career, and taking necessary actions to achieve their career goals. Career planning is an important aspect of human resource management, and it is a new trend in recent research to use the theory and method of psychology to study it. This project understands the current situation of contemporary college students' career planning through investigation and finds the common problems in career planning among college graduates. Systematic analysis of these problems can be used as the research premise to study college students' career planning, help college students to make correct self-assessment and career orientation, improve their self-awareness and evaluation ability, and form a good career self. In the research, the author found that, in the guidance of college students' career development direction, it is necessary to predict the future career development direction of college students. Effective prediction methods can provide objective and simple data support and theoretical basis for educators to help them implement education guidance smoothly. Therefore, this paper has carried out a series of research work. The author firstly analyzes the problems existing in the process of college career education, then analyzes the relevant theories and common methods of educational data mining and the dimension analysis of the influencing factors of college students' career development direction, so as to determine the research dimension of this paper, and then carries out the relevant research such as the modeling of prediction model, the implementation of algorithm, and the case test of prediction method. According to the above research, the author draws the following conclusions: the grey prediction algorithm has better performance in small- and medium-sized data prediction. Based on the comprehensive quality evaluation data, the prediction conclusion is more objective and accurate. However, there are still some shortcomings in the algorithm design in this study. In the future research, we can try to adjust the relevant parameters through more data and more experimental verification, so as to further optimize the algorithm.

Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms

Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.

Quantitative Modeling of Science and Technology Finance Supporting Industrial Innovation Development Based on Internet of Things Technology

Objectives: In recent years, science and technology financial support industries are actively supporting the innovation and development of high-tech industries. In order to test the actual effect of S&T financial support industry support plan, a GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model is designed by using K-means (K-means clustering) algorithm and GM (1,1) (grey prediction) algorithm, which can quantitatively display the development of S&T financial industry to promote high-tech. The GARCH model is used to quantify the degree of innovation and development of science and technology finance industry in the Internet of Things (loT) technology. Finally, according to the quantitative data obtained by GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model, the actual effect of science and technology finance industry promoting innovation and development of high-tech is evaluated by FAHP (Fuzzy Analytic Hierarchy Process) model. The results show that science and technology finance industry plays a positive role in promoting the innovation and development of loT technology.

Prediction of Fish School based on Neural Network and Gray Prediction

Rising ocean temperatures affect the distribution of fish. The livelihoods of many small Scottish fishing companies have also been affected. We designed a fish migration prediction model, a system that predicts fish migration and helps small fishing companies make decisions.First, we creatively proposed the neural network algorithm modified to establish habitat evaluation model based on previous environmental data. Then, the gray prediction algorithm is used to predict the environmental data of each water area in the next 50 years, and the best habitat location of each year is evaluated by the evaluation model based on the environmental data. The migration route of the fish can be obtained by summarizing the best habitats each year in chronological order. We use the knowledge of graph theory to establish a "fishing judgment model" to determine whether small fisheries companies can catch fish in their fishing range. Then we use a triple nested loop to establish a continuous time calculation model, and use this model to discuss the continuous fishing time of a fishing company located in a certain place from a certain year in the future

A comparative study of pick degradation prediction based on grey prediction and Bayesian algorithm

The performance of the pick gradually degrades under the cyclic complex load, so it is difficult to realize the state prediction, in order to be able to accurately predict the pick degradation state, this paper develops a test system for the wear degradation of pick, the method of experimental analysis and numerical simulation is adopted, Multi-signal fusion model of vibration and acoustic emission was constructed, the grey prediction and the Gamma method of Bayesian parameter updating are used to realize the application of the degenerate data, the results prove that the relative error of grey prediction under vibration signal is only 0.45%, the relative error of the Gamma model was 0.57%, the relative error of the Gamma model under Bayesian updating was 0.22%, the three models have good prediction accuracy, the prediction error of the Gamma model under Bayesian updating is minimal.

Substation DC system grounding fault prediction method

There may be disturbance and uncertainty in the collection of leakage current in DC system of substation, which leads to the decrease of accuracy and increase of prediction error. Based on this, an improved grey prediction method is proposed to predict DC system branch grounding fault. Firstly, the characteristics of DC system ground fault parameters are collected. Secondly, the improved grey prediction algorithm is used to predict and estimate whether the detection reaches the fault threshold in the future. Finally, the validity of the proposed method is verified by MATLAB modeling.

Neural Network Ship PID Control and Simulation Based on Grey Prediction

Song, Q.H. and Wang, D.D., 2020. Neural network ship PID control and simulation based on grey prediction. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 299–303. Coconut Creek (Florida), ISSN 0749-0208.Traditional PID is difficult to be applied in large inertial system. It is determined by a large number of engineering experiments, which brings great limitations to the practical application of PID; and the traditional PID control algorithm cannot be applied to the load change, so the control results is always not good enough to be used in the precision requirement. In the paper, the grey prediction control and neural network are combined, and a neural network ship PID control strategy based on grey prediction is proposed, since the grey prediction algorithm needs a small amount of data and small computation load. The simulation results show that the new ship PID self-tuning algorithm is feasible and effective. It can be used in the change of the target parameters, and has strong anti-interference and reliability.

Adaptive Fractional-Order Operator Clock Synchronization Algorithm Based on Grey Prediction

As a result of the influence of clock drift and uncertainty delay in synchronous message transmission, the clock synchronization model based on statistical distribution cannot accurately describe clock deviation. This model also requires a large number of timestamp samples that cause a storage occupation issue for wireless sensor nodes with limited resources. The modeling method based on grey prediction has advantages of low sample demand and simple modeling process. However, the accuracy of the existing clock synchronization models needs to be improved. Based on the grey prediction theory, this paper proposes an adaptive fractional-order operator clock synchronization algorithm considering uncertainty delay. First, based on the clock model and clock offset model, the frequency offset between nodes is optimized by taking the mean on the clock frequencies. Second, a grey prediction algorithm based on a fractional-order operator is proposed by estimating the uncertainty delay in message transmission to obtain the clock offset. Finally, the order of the fractional-order accumulation is adjusted adaptively in the grey prediction model according to the collected timestamp sample values so that the estimation of the uncertainty delay is more accurate, thereby improving the accuracy of the clock offset. Compared with the first-order accumulative grey prediction clock synchronization algorithms and timing-sync protocol for sensor networks, the proposed scheme improved the synchronization accuracy by 29.18% and 44.01%, respectively, and reduced the variance of the clock offset by 48.66% and 64.89%. Thus, the proposed algorithm is characterized by improved stability.

Dynamic quality prediction and control in rotary sponge iron kilns

The quality of sponge iron produced in the coal-fired rotary kilns at TATA Steel Long Products Limited (TSLPL) is permitted to vary in a small window of 80-83% Fe. In order to adhere to this window, the expected quality in the next hour is predicted, and appropriate actions are taken if changes or rates of change that take place are not in the right direction. Airflow rate through the primary air blower is controlled constantly. For quality prediction and control purposes, the One Step Ahead Adaptive Control and Prediction Algorithm (OSAA) and Grey (1,1) prediction algorithm have been tailored and tuned. The working principle of OSAA and Grey(1,1) are described, and their accuracy is compared. The results show that OSAA performs better than the Grey(1,1). The OSAA is therefore used to predict the changes required in volume of air blown through the primary air blower and also the amount of injection coal. An expert control system has been developed based on allowable windows of control variables.