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TIME SERIES neural network model FOR HYDROLOGIC FORECASTING

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Transactions of Tianjin University 2001年第3期7卷 182-186页

作者：钟登华刘东海 Mittnik Stefan 天津大学建筑工程学院天津300072 德国基尔大学计量经济与统计研究所

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper *** paper presents a time series neural network model with back propagation procedure for hydrologic *** from the disadvantages of previous models,the model can be parallel to operate information flexibly and *** excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic *** using directly a training process based on a set of previous data, the model can forecast the time series of stream ***,two practical examples were used to test the performance of the time series neural network *** confirm that the model is efficient and feasible.

关键词： hydrologic forecasting time series neural network model back propagation

Establishment of neural network Prediction model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment

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Journal of Iron and Steel Research International 2012年第6期19卷 25-29页

作者： ZHANGHui—ning XUAn-jun CUIJian HEDong—feng TIANNai——yuan StateKeyLaboratoryofAdvancedMetallurgy UniversityofScienceandTechnologyBeijingBeijing100083China； SchoolofMetallurgicalandEcologicalEngineering UniversityofScienceandTechnologyBeijingBeijing100083China NingboIronandSteelCoLtd Ningbo315000Zh@angChina

In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then sim- ulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 56.45~~ when error is within plus or minus 5%, and the value is 100% when within ~10%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 6. 839o//oo. Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative tempera- ture based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and nraeticahle.

关键词： grey theory correlation degree dephosphorization terminative temperature neural network model

An improved neural network model for prediction of mechanical properties of magnesium alloys

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中国科学:技术科学 2010年第4期 452-452页

作者： TANG AiTao 1,2,LIU Bin 1,PAN FuSheng 1,2,ZHANG Jing 1,2,PENG Jian 1,2 & WANG JingFeng 1,2 1 College of Materials Science & Engineering,Chongqing University,Chongqing 400044,China 2 National Engineering Research Center for Magnesium Alloys,Chongqing University,Chongqing 400044,China

An improved neural network model was developed for prediction of mechanical properties in the design and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable *** results showed that the improved model apparently decreased the prediction errors,and raised the accuracy of the prediction *** preprocessing parameters were found to be[0.15,0.90]for the tensile strength,[0.1,0.9]for the yield strength,and[0.15,0.90]for the *** the above parameters were used,the relativity for predicition of strength was bigger than *** using improved ANN analysis,more reasonable process parameters and composition could be obtained in some magnesium alloys without addition of strontoum.

关键词： magnesium alloys neural network model composition mechanical properties

Analysis of spatial distribution characteristics and driving factors of ethnicminority villages in China using geospatial technology and statistical models

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Journal of Mountain Science 2024年第8期21卷 2770-2789页

作者： SHAO Dandan ZOH Kyungjin Interdisciplinary Program in Landscape Architecture Graduate School of Environmental StudiesSeoul National UniversitySeoul 08826Korea Graduate School of Environmental Studies Seoul National UniversitySeoul 08826Korea

This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial *** are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient *** are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization *** this study,1652 EMV in China were selected as the research *** Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority *** network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in *** results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest *** EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent

关键词： Ethnic-Minority Villages Spatial structure Settlement geography neural network model Spatial econometric model GeoDetector

Improved dynamical model for neural network and the stabilization of associative memory

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Progress in Natural Science:Materials International 1998年第5期 99-107页

作者：李耀勇郑南宁

A new neural network model with more biological background is presented. Besides the usual prop. erties of internal potential, accumulation and outpuf, the new model is endowed with some dynamical factors such as local potential accumulation, adaptive variation of the threshold, the lateral links among the corresponding neurons ,is well as the adaptive change of the link weights in the evolution of the network. The typical dynamics and characteristics of associative memory can also be demonstrated through varying the values of the parameters in the neural net-work.

关键词： neural network model nonlinear dynamics associative memory unstable orbits control.

Optimized shearing strategy for heavy plate based on contour recognition

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Journal of Iron and Steel Research International 2023年第9期30卷 1821-1833页

作者： Jian-zhao Cao Yu-xia Wang Shao-wen Huang Chang-tao Wang School of Computer Science and Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina Shandong Iron and Steel Co. Ltd.Jinan 271105ShandongChina School of Electrical and Control Engineering Shenyang Jianzhu UniversityShenyang 110168LiaoningChina

The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was ***,multi-array binocular vision linear cameras were used to complete the image ***,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour ***,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was *** practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel *** maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 *** correlation coefficient of the length prediction model based on the back propagation neural network is very *** reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.

关键词： Heavy plate Contour detection Intelligent shearing strategy Length prediction neural network model

Experimental and numerical analysis of beam to column joints in steel structures

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Frontiers of Structural and Civil Engineering 2018年第4期12卷 642-661页

作者： Gholamreza ABDOLLAHZADEH Seyed Mostafa SHABANIAN Faculty of Civil Engineering Babol Noshirvani University of TechnologyBabolIran

The behaviors such as extreme non-elastic response, constant changes in roughness and resistance, as well as formability under extreme loads such as earthquakes are the primary challenges in the modeling of beam-to-column connections. In this research, two modeling methods including mechanical and neural network methods have been presented in order to model the complex hysteresis behavior of beam-to-column connections with flange plate. First, the component-based mechanical model will be introduced in which every source of transformation has been shown only with geometrical and material properties. This is followed by the investigation of a neural network method for direct extraction of information out of experimental data. For the validation of behavioral curves as well as training of the neural network, the experiments were carried out on samples with real dimensions of beam-to-column connections with flange plate in the laboratory. At the end, the combinational modeling framework is presented. The comparisons reveal that the combinational modeling is able to display the complex narrowed hysteresis behavior of the beam-to-column connections with flange plate. This model has also been successfully employed for the prediction of the behavior of a newly designed connection.

关键词： beam to column connections experiments component method neural network model combinational modeling

Examining the efficacy of revegetation practices in ecosystem restoration programs:insights from a hotspot of sandstorm in northern China

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Frontiers of Earth Science 2021年第4期15卷 922-935页

作者： Ziqiang DU Rong RONG Zhitao WU Hong ZHANG Institute of Loess Plateau Shanxi UniversityTaiyuan 030006China 2 Shanxi Key Laboratory for Ecological restoration of Loess Plateau Taiyuan 030006China Field Scientific Observation and Research Station of the Ministry of Education of Shanxi Subalpine Grassland Ecosystem Taiyuan 030006China College of Environmental&Resource Sciences Shanxi UniversityTaiyuan 030006China

Retrospectively evaluating the efficacy of revegetation practices is helpful in planning and imple-menting future ecosystem restoration programs(ERP).Having a good understanding of how human activities can affect vegetation cover,both before and after ERP,is particularly important in sandstorm hotspot *** Beijing-Tianjin Sandstorm Source Region(BTSSR)is one such *** conducted an investigation into vegetation dynamics within the *** was done using remote sensing data in conjunction with climate data sets and land use data spanning the 1982-2014 *** relationships between climatic factors(such as precipitation and temperature),and vegetative change were modeled using a neural network *** a process of residual analysis,the proportions of human-induced vegetative change both before and after the ERP were *** results show that:1)before the ERP(1982-2000),40.96%of the study area exhibited significantly progressive vegetation changes(p<0.05).This proportion decreased to encom-pass only 20.23%of the study area in the period following the ERP(2001-2014).2)89.55%of the study area showed signs of human-induced vegetation degradation before the *** 2001 and 2014 however,following ERP,this figure fell to only 27.78%.3)ERP implementation led to visible improvements in vegetative conditions within the BTSSR,especially in areas where ecological restoration measures were directly and anthropogenically *** results highlight the benefits that positive human action(i.e.,revegetation initiatives implemented under the framework of an ERP)have brought to the BTSSR.

关键词： vegetation dynamics human activities ERP neural network model Beijing-Tianjin sandstorm source region

Identification of Driving Intention Based on EEG Signals

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Journal of Beijing Institute of Technology 2018年第3期27卷 357-362页

作者： Min Li Wuhong Wang Xiaobei Jiang Tingting Gao Qian Cheng School of Mechanical Engineering Beijing Institute of Technology School of Automobile & Transportation Qingdao University of Technology

The driver’s intention is recognized by electroencephalogram(EEG)signals under different driving conditions to provide theoretical and practical support for the applications of automated *** EEG signal acquisition system is established by designing a driving simulation experiment,in which data of the driver’s EEG signals before turning left,turning right,and going straight,are collected in a specified time *** collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters.A driving intention recognition model,based on neural network,is established,and particle swarm optimization(PSO)is adopted to optimize the model *** extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left,turning right,and going *** is used to simulate and verify the established model to obtain the results of the *** maximum recognition rate of driving intention is 92.9%.Results show that the driver’s EEG signal can be used to analyze the law of EEG ***,the PSO-based neural network model can be adapted to recognize driving intention.

关键词： wavelet packet electroencephalogram(EEG)signal driving intention neural network model