scholarly journals Prediction and Early Warning of Regional Coordinated Development Based on Convolution Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xue Wen
Keyword(s):  
Neural Network ◽  
Evaluation Model ◽  
Development Trend ◽  
Urban Agglomeration ◽  
Convolution Neural Network ◽  
Western China ◽  
Good Prediction ◽  
Coordinated Development ◽  
Prediction Ability ◽  
Coordination Degree

In order to respond to the regional coordinated development of the country, it is necessary to put forward a method that can predict and analyze the development trend according to the current development situation. In view of this, the research will carry on the present situation and forecast analysis to the coordinated development of urban agglomeration in Western China. Firstly, the 3E system is used to establish the regional coordination degree evaluation model, and on this basis, the ellipsoid model is introduced for better coordination degree evaluation. In addition, in order to improve the prediction ability of the model, the convolution neural network is used to realize the big data analysis of the model. The results show that the overall coordination degree of the western urban agglomeration is in a weak coordination state in 2015, but the coordination degree of the region will reach 147.35 in 2020. The results show that the overall coordination degree of western urban agglomeration will gradually show a good trend, but the change speed is slow. The above results show that the prediction model in the study has strong practicability, the calculation results can fit the current situation, and the good prediction ability can provide decision-making suggestions for many governments.

scholarly journals An Empirical Study on the Coordinated Development of Economy and Ecological Environment of Eastern City Group in Qinghai Province

2021 ◽  
Vol 236 ◽  
pp. 03015
Author(s):  
Yatian Liu ◽  
Shengxi Ding
Keyword(s):  
Economic Development ◽  
Environmental Economic ◽  
Entropy Method ◽  
Urban Agglomeration ◽  
Ecological Environment ◽  
Coordinated Development ◽  
Qinghai Province ◽  
Development Level ◽  
Comprehensive Development ◽  
Coordination Degree

Firstly, this article uses the Entropy method to calculate the weights of economic development and ecological environment indicators in the eastern urban agglomeration of Qinghai Province from 2005 to 2019. Secondly, this article uses the calculated weights and linear weighting functions to construct evaluation models for economic development and ecological environment development, respectively. The results show that the comprehensive development level of the economic development in the eastern urban agglomeration of Qinghai Province cities is gradually rising, and the comprehensive development level of the ecological environment fluctuates slightly but the overall development trend is increasing. Then, using the Environmental-Economic Coordination degree evaluation model, quantitative analysis and evaluation of the Environmental-Economic system coordination degree, it is found that the coordinated development of the economic and ecological environment of the eastern urban agglomeration in Qinghai Province is relatively well. Finally, it analysis and proposes countermeasures and suggestions to promote the coordinated development of the economic and environmental system of the eastern urban agglomeration in Qinghai Province.

scholarly journals Locating and Tracking Model for Language Radiation Transmission Based on Neural Network and FAHP

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
SongGui Zhu ◽  
Hailang He ◽  
Yuanyuan Zheng
Keyword(s):  
Neural Network ◽  
Second Language ◽  
Development Trend ◽  
Analytic Hierarchy ◽  
Prediction Ability ◽  
Internal Parameters ◽  
Tracking Model ◽  
Development Of Language ◽  
Global Language ◽  
Hierarchy Process

With the development of internationalization, the distribution of languages and the office addresses of multinational companies are changing constantly. This paper makes the following research and exploration on this phenomenon: impact on the development of languages around the world. This paper studies the changes of native and second-language users and uses the historical data to predict the development trend by using the gray number series prediction model. Get the types of factors that affect the second language. Then, use fuzzy analytic hierarchy process to calculate the score of each factor. Finally, the global language trend equation is simulated: predictions for the development of language. In this paper, radiation propagation is calculated, and the method of CNN neural network is used to train big data, and the language trend positioning equation is drawn. Finally, the optimal language is obtained by using wavelet analysis and linear programming at different addresses. About model checking, according to the model’s internal prediction ability and the significance of internal parameters, it is concluded that the model has high practicability, sensitivity, and stability.

scholarly journals Predicting Hidden Danger Quantity in Coal Mines Based on Gray Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 622
Author(s):  
Hongze Zhao ◽  
Qiao He ◽  
Zhao Wei ◽  
Lilin Zhou
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Large Scale ◽  
Safety Management ◽  
Coal Mines ◽  
Development Trend ◽  
Good Prediction ◽  
Average Value ◽  
Normal Distribution Curve ◽  
Safety Production

The hidden danger is the direct cause of coal mine accidents, and the number of hidden dangers in a certain area not only reflects the current safety situation, but also determines the development trend of safety production in this area to a large extent. By analyzing the formation and development law of the hidden dangers and hidden danger accident-induced mechanism in coal mines, it is concluded that there are some objective laws in the process of occurrence, development, weakening, and even stabilization of hidden dangers in a certain area. The development of the number of hidden dangers for a coal mine generally presents the law of similar normal distribution curve, with a certain degree of partial symmetry. Many years of hidden danger elimination in coal mines will accumulate large-scale hidden danger data. In this paper, by using the average value of hidden danger quantity in consecutive months to weaken the oscillation of hidden danger quantity sequence, and combining with gray model (1,1) and the neural network of extreme learning machine, and employing big data of hidden dangers available, a hidden danger quantity prediction model based on the gray neural network was established, and the experimental analysis and verification carried out. The results show that the model can achieve good prediction effect on the number of hidden dangers in a coal mine, which not only reflects the complex gray system behavior of hidden dangers of a coal mine, but also can predict dynamically. The safety management efficiency and emergency capacity of the coal mine enterprise will be greatly improved.

scholarly journals Internal Coordinated Development of China's Urbanization and its Spatiotemporal Evolution

2019 ◽  
Vol 11 (3) ◽  
pp. 626 ◽  
Author(s):  
Liangzhen Zang ◽  
Yiqing Su
Keyword(s):  
Growth Rate ◽  
Spatial Distribution ◽  
Eastern China ◽  
River Delta ◽  
Spatial Data Analysis ◽  
Western China ◽  
Spatiotemporal Evolution ◽  
Coordinated Development ◽  
Urbanization Development ◽  
Coordination Degree

High urbanization quality, predominantly determined by the degree of internal coordinated development, is the most important factor in promoting urbanization development. Based on the panel data of 283 Chinese cities from 2007 to 2016, this paper analysed the internal coordination degree, spatial distribution and spatiotemporal evolution of urbanization using the methods of range standardization, entropy, and coupling and coordination models, as well as exploratory spatial data analysis. We found the following results: (1) The internal coordination degree of China’s urbanization was at a low level for a long time, but it presented a gradually increasing trend; (2) The internal coordinated development of urbanization exhibited an obvious spatial agglomeration. Specifically, it displayed a stepped pattern with a higher internal coordination degree in eastern China, a lower degree in western China, and a spatial distribution with multi-centre agglomeration and diffusion. (3) The spatial distribution of the internal coordinated development of urbanization was stable in most regions of China, only changing in a few. (4) The growth rate of the internal coordination degree of China’s urbanization presented the pattern of a dotted distribution, while the growth rate in western China was higher than in central and eastern China. The spatiotemporal evolution relates to the policies changes of China’s urbanization. In particular, the urbanization in China transfers its focus from population transfer to the development of quality from 2007 to 2016. To promote the sustainable and healthy development of China's new urbanization, Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta shall focus on accelerating the transformation of economic development mode; The provinces located around the above three regions shall strengthen the upgrading and promotion of basic public services. The northeast and central provinces shall speed up the innovation of systems and mechanisms and gradually release the potential of urbanization development by promoting the mobility of urban population, and the vast majority of provinces in Western China shall further improve the spatial development potential for urbanization.

Financial Crisis Warning Analysis for Companies Based on BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1529-1533
Author(s):  
Hong Min Zhang
Keyword(s):  
Neural Network ◽  
Financial Crisis ◽  
Capital Market ◽  
Bp Neural Network ◽  
Common Factor ◽  
Principal Component ◽  
Special Treatment ◽  
Good Prediction ◽  
Prediction Ability ◽  
Financial Status

This paper chose 11 financial criteria based on the principle for criteria selection. Due to the relevance of criteria, this paper collected 5 common factors with the method of principal component analysis of factor. The comprehensive score of every sample is got from the score of every common factor. Considering whether enterprises are special treated as auxiliary reference, 3 ranges of score are divided into 3 financial status accordingly: health, focus, crisis, disregarding the division criteria of special treatment or otherwise.Finally, the network got trained and tested by regarding 5 common factor as input and the financial status as output. It is found that BP neural network has a good prediction ability, high accuracy and application value, which will give a great assistance to the development of capital market in our country.BP: financial crisis warning, empirical analysis, factor analysis, BP neural network.

Study on Coastal Zone Sustainable Development and its Application

2012 ◽  
Vol 170-173 ◽  
pp. 2280-2283 ◽  
Author(s):  
Hong Tao Nie ◽  
Jian Hua Tao ◽  
Min Du
Keyword(s):  
Coastal Zone ◽  
Development Trend ◽  
Efficiency Index ◽  
Bohai Bay ◽  
Coordinated Development ◽  
Urbanization Development ◽  
Rapid Industrialization ◽  
Set Up ◽  
The Development Trend ◽  
Coordination Degree

Rapid industrialization and urbanization development had brought a serious challenge for coastal zone in China. A coastal management model had been set up for coastal zone, the coordination degree and coordinated development model were adopted to analyze the development trend. The results show that the gap between the environment comprehensive efficiency index and economic efficiency index is getting bigger and bigger for Bohai Bay coastal zone.

scholarly journals The Coupling Coordinated Relationship Between New-type Urbanization, Eco-Environment and its Driving Mechanism: A Case of Guanzhong, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Liyuan Dong ◽  
Juan Shang ◽  
Rizwan Ali ◽  
Ramiz U Rehman
Keyword(s):  
Driving Force ◽  
Driving Forces ◽  
Urban Agglomeration ◽  
Ecological Environment ◽  
Driving Mechanism ◽  
Coordinated Development ◽  
Environment Quality ◽  
New Type ◽  
Coordination Degree

As an important platform for participating in international competition and cooperation, supporting economic growth and promoting coordinated regional development, urban agglomeration plays an important role in China’s economic, social and urbanization development. At this time, the Guanzhong Plain urban agglomeration (GZPUA), as the second largest urban agglomeration in western China, has a moderate population density. The high demand and high input of resources for population growth make the regional ecological destruction and environmental pollution more prominent. Therefore, it is of great practical significance to study the coordinated development of urbanization and ecological environment in GZPUA. By using the panel data of the GZPUA of China between 2008 and 2017, this study constructed evaluation index system of new-type urbanization and ecological environment quality and calculated the weights of the indices within the evaluation system via the improved entropy weight method, finally determined the new-type urbanization and ecological environment quality of each city. Then the coupling coordination degree model was used to analyze the coupling coordination relationship between two systems of GZPUA and their coupling stages and levels. In addition, the driving mechanism of their coordination degree was explored by using geographic detector method. The results show that: 1) The GZPUA new-type urbanization quality is characterized by both slow growth except Xi’an by a rapid increase. The ecological environment quality is characterized by both slow growth and fluctuations, except Qingyang by a decrease. There are spatial differences between the quality of new-type urbanization and the quality of ecological environment. 2) The 11 cities can be divided into high-high type (Xi’an), high-low type (Xianyang, Yuncheng, Linfen), low-low type (Pingliang, Weinan), and low-high type (Shangluo, Tianshui, Qingyang), different types should take different development paths. 3) The coordination degree between urbanization and ecological environment quality in GZPUA showed an upward trend, and formed a spatial distribution pattern with Xi’an as the core and decreasing to the outer circle cities, with regional differences. 4) The coordinated development of new-type urbanization and ecological environment is a process in which various driving factors act on different driving forces. These driving forces can be summarized as market driving force, endogenous driving force, outward driving force and administrative driving force. Based on the current situation of coordinated development of new-type urbanization and ecological environment in the GZPUA, it is recommended to promote the coordinated development of urbanization and ecological environment according to local conditions, strengthen the urbanization market mechanism, and optimize the industrial layout. Further, guide the flow of various factors across regions, strengthen technological innovation on the basis of breaking regional divisions, narrow the gap between urban and rural areas, establish the concept of coordinated development, and give play to the government’s “visible hand” role.

An Empirical Study on the Coordinated Development of Chinese Greenfood Industry Based on DEA Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 936-942
Author(s):  
Guo Yu Song
Keyword(s):  
Evaluation System ◽  
Evaluation Model ◽  
Heilongjiang Province ◽  
Coordinated Development ◽  
Development Status ◽  
System A ◽  
Index Evaluation ◽  
Dea Analysis ◽  
Index Evaluation System ◽  
Coordination Degree

The paper constructed an coordination evaluation model which measures the coordinated development status of the development system of Chinese greenfood industry and its subsystems,and established an index evaluation system from the perspective of system. A DEA approach is used to provide evidence for the coordinated development of the scale subsystem and the structure-efficiency subsystem based on the data from the greenfood industry in Heilongjiang province. The results suggest that there exists a dramatic fluctuation of the coordination of the greenfood industry system and its subsystem,and that the coordination degree of the greenfood industry is not so satisfying,in particular there is a downtrend in the coordinated development of the greenfood industry. Further research suggest that it’s feasible to transform the mode of industry development and to enhance the coordination degree of the subsystems,especially the structure-efficiency subsystem to realize the coordinated and sustainable development of the Chinese greenfood industry.

Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on BP Neural Network

2020 ◽  
Author(s):  
Hong-Wei He ◽  
Zao-Jian Zou
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Bp Neural Network ◽  
Mean Squared Error ◽  
Black Box ◽  
Good Prediction ◽  
Prediction Ability ◽  
System Identification Method ◽  
Identification Method ◽  
Maneuvering Motion

Abstract This paper deals with the black-box modeling of 3-DOF nonlinear maneuvering motion of surface ships by using system identification method based on BP neural network. A Mariner Class vessel is taken as the study object. The time series used in training and testing the network is the simulated data of a series of maneuvers, which is obtained by numerically solving the Abkowitz model using fourth-order Runge-Kutta method. A three-layer neural network is built to solve this multivariable regression fitting problem, and only one network model is trained to predict various ship maneuvers. Taking the mean squared error (MSE) as the loss function, the network’s weights are optimized by Levenberg-Marquardt (LM) algorithm effectively. The trained network is evaluated by several simulation tests, and it is shown that the network achieves good prediction ability and can predict the maneuvering motion as long as the control inputs and initial states of the ship motion are known.

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