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Simulating the Inverse Kinematic Model of a Robot through artificial neural networks： Complementing the Teaching of Robotics

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Journal of Mechanics Engineering and Automation 2014年第12期4卷 960-968页

作者： Jose Tarcisio Franco de Camargo Estefano Vizconde Veraszto Gilmar Barreto Department of Compouter Engineering Regional Universitary Center of E. S. do Pinhal E. S. do Pinha113990-000 Brazil Department of Natural Sciences Mathematics and Education Federal University of Sao Carlos Araras 13604-900 Brazil School of Electrical and Computing Engineering State University of Campinas Campinas 13083-852 Brazil

Teaching robotics necessarily involves the study of the kinematic models of robot manipulators. In turn, the kinematics of a robot manipulator can be described by its forward and reverse models. The inverse kinematic model, which provides the status of the joints according to the desired position for the tool of the robot, is typically taught and described in robotics classes through an algebraic way. However, the algebraic representation of this model is often difficult to obtain. Thus, although it is unquestionable the need for the accurate determination of the inverse kinematic model of a robot, the use of ANNs （artificial neural networks） in the design stage can be very attractive, because it allows us to predict the behavior of the robot before the formal development of its model. In this way, this paper presents a relatively quick way to simulate the inverse kinematic model of a robot, thereby allowing the student to have an overview of the model, coming to identify points that should be corrected, or that can be optimized in the structure of a robot.

关键词： Robotics artificial neural networks engineering education.

Estimating Monthly Evaporation Using artificial neural networks

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Journal of Environmental Science and Engineering 2011年第1期5卷 88-91页

作者： B. Boroomand-Nasab M. Joorabian Electrical Engineering Department Engineering Faculty Shahid Chamran University Ahwaz Iran

Predicting evaporation rate is one of important elements for hydrology planning. There are several methods to estimate evaporation from a water surface. The objective of this study was to test the capability of artificial neural networks （ANNs） to predict evaporation using 10 years data set （1999 to 2008） from Ahvaz meteorological station and has been compared with values obtained using pan evaporation. Software Qnet 2000 has been utilized to model the evaporation. The Qnet 2000 was trained with monthly climate data （Solar radiation, minimum and maximum temperature, minimum and maximum relative humidity, and wind velocity） as input. The model was approximately implemented 144 times that finally hyperbolic secant stimulant function of 4 input parameters including minimum temperature, maximum temperature, solar radiation and wind velocity and 6 nodes in hidden layer has been yielded the best outcome. Correlation coefficients （R2） in training and testing sections are to 97.4% and 97.3% respectively. Also maximum errors in training and testing sections equaled to 18% and 24% respectively. Results showed ANNs approach works well for the data set used in this region.

关键词： Evaporation artificial neural networks Ahvaz evaporation pan

Parameter selecting and quality predicting of spot welding based on artificial neural networks

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China Welding 1998年第2期7卷 4-8页

作者：赵熹华王宸煜张若冰

This paper proposes a procedure for using artificial neural networks (ANN) in spot welding , and establishes spot welding parameter selecting ANN systems and spot welding joint quality predicting ANN systems . It has been proved that the ANN systems have high prediction precision , providing a new way of parameter selecting and quality predicting in spot welding .

关键词： artificial neural networks resistance spot welding parameter selecting quality predicting

An improved model based on artificial neural networks and Thevenin model for nickel metal hydride power battery

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An improved model based on artificial neural networks and Th...

2010 International Conference on Optics, Photonics and Energy Engineering (OPEE 2010)

作者： Changhao Piao1,2,Xiaoyong Yang1,3 1Ministry of Education Key Lab of Network Control Tech.& Intelligent Instruments,Chongqing University of Posts and Communications,Chongqing,ChinaCong Teng 3,HuiQian Yang2 2Chongqing ChangAn New Energy Automobile Co.,Ltd.Chongqing,China 3Research Center of Energy Electronics,Chongqing University of Posts and Communications

Based on artificial neural networks and Thenvenin model,this paper uses an improved model predicting state of *** combine artificial neural networks model with Thevenin model,and predict state of charge in real time at the same *** the difference between the predictive value of artificial neural networks model and the predictive value of Thevenin model is more than 10%,we revised the predictive value of artificial neural networks model by weighted average *** results show that it can reduce the error of artificial neural networks model obvious and the average error is 4.72%.It is lower independence on initial state of charge than artificial neural networks model.

关键词： nickel metal hydride power battery state of charge artificial neural networks Thevenin model

Robust Asymptotic Stabilization of Uncertain Nonlinear Systems Using artificial neural networks with Application to Power Systems

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Robust Asymptotic Stabilization of Uncertain Nonlinear Syste...

2009中国控制与决策会议

作者： Ying Zhou~1,Qiang Zang~2 1.College of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210003,China 2.School of Automation,Southeast University,Nanjing 210096,China

＜正＞The problem of robust stabilization for uncertain nonlinear systems is considered in this *** controlled systems are not restricted to the strict feedback form any *** robust stabilization controller is designed based on Backstepping approach with using artificial neural networks（ANN） to account for the uncertain terms.A new adaptive algorithm is proposed to update the weights of ANN such that all signals in the closed-loop systems are bounded and the states are convergent asymptotically to the equilibrium through the proposed controller.A speed governor is designed for single-machine infinite-bus system based on the design scheme proposed in this paper and the simulation results illustrate the utility of the proposed scheme.

关键词： Uncertain Nonlinear Systems artificial neural networks Backstepping Power Systems

Radar Targets Identification Based on Perfect Parametric Modeling Using artificial neural networks

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Radar Targets Identification Based on Perfect Parametric Mod...

2002年IEEE十区计算机、通信、控制和电机工程会议

作者： WANG Jun Institute of Electronic Engineering, China Academy of Engineering Physics P.O. Box 919—515,Mianyang, Sichuan, 621900, CHINA, Fax: 86-816-2487594, E-mail:

In this paper, we have developed a new targetrecognition procedure. In order to efficiently obtain featurevectors for target discrimination, the closed-formexpression of geometrical wave front have been derived toprovide efficient and accurate computation. The resultingsmall dimensional feature vectors based on the developedextractor are identified using the well-known ANNclassifier. The results show that the presented scheme givessuccessful correct automatic target recognition (ATR) in thelow SNR with low computational costs. Although this workconcentrates mainly on parametric modeling ofelectromagnetic target, further work should study moreefficient ANN architecture when the number of candidatesis dramatically increased in the practice.

关键词： Inverse problem automatic target recognition radar image parametric feature vectors artificial neural networks

On the Application of artificial neural networks to Fault Diagnosis in Analog Circuits with Tolerances

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On the Application of Artificial Neural Networks to Fault Di...

2000年第16届世界计算机大会

作者： Deng Ying He Yigang College of Electrical and Information Engineering Hunan University

＜正＞This paper proposes a method for analog fault diagnosis by adopting neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and reduce testing *** proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network,which is shown to be capable of robust diagnosis of analog circuits with tolerances.

关键词： artificial neural networks Analog circuits Fault diagnosis Tolerances

Estimating of Reference Evapotranspiration by Using artificial neural networks（Comparative Study of The Farm of Mo′tamediyeh from Dependencies of Neishabour,The Razavi Khorasan Province,Iran）

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Estimating of Reference Evapotranspiration by Using Artifici...

International Conference on Soft Computing and its Applications(ICSCA’2012);International Conference on Power Electronics,Systems and Applications(ICPESA’2012)International Conference on Mechanical,Nanotechnology and Cryogenics Engineering(ICMNC’2012);International Conference on Arts and Business Information Systems(ICAEBIS’2012);International Conference on Chemical,Biological and Medical Sciences(ICCBMS’2012);International Conference on Transport,Environment and Civil Engineering(ICTECE’2012)

作者： Sayyed Ali Moasheri Peyman Afrasiab Shirahmad sarani Noushin Sarani Irrigation and Drainage Department University of Zabol Department of water and soil University of Zabol Faculty of Agriculture University of Zabol-Zabol Irrigation and Drainage Department University of Zabol

Today progress of artificial intelligence science in the different sciences specially agricultural sciences is be very important. If there is not specific relationship between effective factors for creating of a phenomenon, it appears. The reference evapotranspiration is a phenomenon that experimental relationships for calculating of it has be expressed up to now. Using of these relationships requires complicated calculations and more important than all requires calibration for every region. Accuracy of these relationships varies in according to climate and studying region position. We have modeled and predicted values of reference evapotranspiration for grass plant of a farm in Mo′tamediyeh village located Neishabour town by using of artificial neural network in the research. The results of the research in addition to high compatibility of modeling and predicting with correlation coefficient 0.997 can analyze sensitivity and affecting every parameters of effective of the grass reference evapotranspiration values.

关键词： artificial neural networks Estimating Reference Evapotranspiration Sustainability

artificial neural networks in Risk Management: A Bibliometric Study

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Journal of Business Administration Research 2021年第1期4卷 29-36页

作者： Breno Gontijo Tavares Carlos Eduardo Sanches da Silva Adler Diniz de Souza INATEL Nacional Institute of TelecommunicationsInformation Technology and Communications CenterAv.João de Camargo510CentroZIP:37540-000Santa Rita do SapucaíMinas Gerais StateBrazil UNIFEI Secretaria de Educação Superior do Ministério da Educação(SESU/MEC)Esplanada dos MinistériosBl“L”-3ºAndarZIP:70047-900BrasíliaDistrito Federal StateBrazil UNIFEI Federal University of ItajubaInstitute of Mathematics and ComputingBPS Ave.1303PinheirinhoZIP:37.500-903ItajubaMinas Gerais StateBrazil

This study presents a bibliometric analysis of artificial neural networks in Risk *** study considered articles from the *** of Knowledge and Scopus databases,Identification of publishers,countries,periodicals and the keywords most frequently *** used the CiteSpace® software to analyze this material,which provides a set of features to support bibliometrics,including the reference *** study provides data collection on artificial neural networks applied to risk *** number of works identified in this study is significant,and in the last ten years,the number of citations has *** did not identify the increase in paper count within the same period.

关键词： Bibliometric study artificial neural networks Risk management I.S.I.Web of knowledge Scopus