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Model Identification of Water Purification Systems Using RBF Neural Network
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作者 徐立新 《Journal of Beijing Institute of Technology》 EI CAS 1998年第3期293-395,296-298,共6页
Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build... Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow CODM can be acquired under the inflow CODM condition. Results The improved self-organized learning algorithm can assign the centers into appropriate places , and the RBF network's outputs at the sample points fit the experimental data very well. Conclusion The model of ozonation /BAC system based on the RBF network am describe the relationshipamong various factors correctly, a new prouding approach tO the wate purification process is provided. 展开更多
关键词 rbf neural network: identification OZONE biological activated carbon
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Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor 被引量:9
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作者 杨敬锋 李亭 +1 位作者 卢启福 陈志民 《Agricultural Science & Technology》 CAS 2010年第2期140-142,共3页
Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content senso... Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective... 展开更多
关键词 Calibration Model Soil Moisture Sensor Wireless Sensor networks rbf neural networks
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Adaptive RBF neural network control of robot with actuator nonlinearities 被引量:5
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作者 Jinkun LIU, Yu LU (School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China) 《控制理论与应用(英文版)》 EI 2010年第2期249-256,共8页
In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinear... In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion. 展开更多
关键词 Adaptive control rbf neural network Actuator nonlinearity Robot manipulator DEADZONE
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Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network 被引量:6
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作者 Dongyang Shang Xiaopeng Li +2 位作者 Meng Yin Fanjie Li Bangchun Wen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第4期203-226,共24页
The length of fexible manipulators with a telescopic arm alters during movement.The dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in length.W... The length of fexible manipulators with a telescopic arm alters during movement.The dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in length.With an increase in the manipulators’length,the nonlinear terms caused by fexibility in the manipulators’dynamic equations cannot be ignored.The time-varying characteristics and nonlinear terms of telescopic fexible manipulators cause fuctuations in rotation angles,which afect the operation accuracy of end-efectors.In this study,a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of fexible telescopic manipulators.First,the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle,and the infuence of nonlinear terms is analyzed.Subsequently,a combined control strategy is proposed to suppress the fuctuation of the rotation angle in telescopic fexible manipulators.The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators.Fuzzy rules are utilized to adjust the controller parameters in real-time.The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the fexible manipulators.The uncertain part comprises time-varying parameters and nonlinear terms.Finally,numerical simulations and prototype experiments prove the efectiveness of the combined control strategy.The results prove that the proposed control strategy has a smaller standard deviation of errors.Therefore,the combined control strategy is more suitable for telescopic fexible manipulators,which can efectively improve the control accuracy of rotation angles. 展开更多
关键词 Flexible manipulator rbf neural network Fuzzy control Dynamic uncertainty
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Prediction of Free Lime Content in Cement Clinker Based on RBF Neural Network 被引量:5
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作者 YUAN Jingling ZHONG Luo +1 位作者 DU nongfu TAO Haizheng 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2012年第1期187-190,共4页
Considering the fact that free calcium oxide content is an important parameter to evaluate the quality of cement clinker, it is very significant to predict the change of free calcium oxide content through adjusting th... Considering the fact that free calcium oxide content is an important parameter to evaluate the quality of cement clinker, it is very significant to predict the change of free calcium oxide content through adjusting the parameters of processing technique. In fact, the making process of cement clinker is very complex. Therefore, it is very difficult to describe this relationship using the conventional mathematical methods. Using several models, i e, linear regression model, nonlinear regression model, Back Propagation neural network model, and Radial Basis Function (RBF) neural network model, we investigated the possibility to predict the free calcium oxide content according to selected parameters of the production process. The results indicate that RBF neural network model can predict the free lime content with the highest precision (1.3%) among all the models. 展开更多
关键词 rbf neural network cement clinker free lime content
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Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network 被引量:5
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作者 刘震涛 费少梅 《Journal of Zhejiang University Science》 CSCD 2004年第8期960-965,共6页
Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CN... Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data. 展开更多
关键词 Dual fuel engine Emission performance rbf neural network
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Identification of TSS in the Human Genome Based on a RBF Neural Network 被引量:1
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作者 Zhi-Hong Peng Jie Chen Li-Jun Cao Ting-Ting Gao 《International Journal of Automation and computing》 EI 2006年第1期35-40,共6页
The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites ... The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites (TSSs) in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C++ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy. 展开更多
关键词 Promoter recognition human genome transcription start site rbf neural network.
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Nuclear power plant fault diagnosis based on genetic-RBF neural network 被引量:1
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作者 SHI Xiao-cheng XIE Chun-ling WANG Yuan-hui 《Journal of Marine Science and Application》 2006年第3期57-62,共6页
It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neu... It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved. 展开更多
关键词 geneticalgorithm (GA) rbf neural network nuclear power plant
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Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network 被引量:3
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作者 LUO Yue LIU Yu-Nan +1 位作者 LIN Bing WEN Chuan-Biao 《Digital Chinese Medicine》 2020年第1期11-19,共9页
Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural n... Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification. 展开更多
关键词 TCM constitution Physical examination index Correlation model rbf neural network
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ROLS-AWS algorithm used in RBF neural network for multi-user detection
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作者 王永建 赵洪林 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第4期553-557,共5页
To improve the computational speed, the ROLS-AWS algorithm was employed in the RBF based MUD receiver. The radial basis function was introduced into the multi-user detection (MUD) firstly. Then a three-layer neural ... To improve the computational speed, the ROLS-AWS algorithm was employed in the RBF based MUD receiver. The radial basis function was introduced into the multi-user detection (MUD) firstly. Then a three-layer neural network demodulation spread-spectrum signal model in the synchronous Gauss channel was given and the multi-user detection receiver was analyzed intensively. Simulations by computer illustrate that the proposed RBF based MUD receiver employing the ROKS-AWS algorithm is better than conventional detectors and common BP neural network based MUD receivers on suppressing multiple access interference and near-far resistance. 展开更多
关键词 multi-user detection (MUD) rbf neural network ROLS-AWS algorithm
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Global approximation based adaptive RBF neural network control for supercavitating vehicles 被引量:11
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作者 LI Yang LIU Mingyong +1 位作者 ZHANG Xiaojian PENG Xingguang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第4期797-804,共8页
A global approximation based adaptive radial basis function(RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles(SV).A nominal model is built firstly wit... A global approximation based adaptive radial basis function(RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles(SV).A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller(CTC) for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation. 展开更多
关键词 radial basis function rbf neural network computedtorque controller (CTC) adaptive control supercavitating vehicle(SV)
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Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm 被引量:11
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作者 XI Zhifei XU An +2 位作者 KOU Yingxin LI Zhanwu YANG Aiwu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第2期498-516,共19页
Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a ta... Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model. 展开更多
关键词 trajectory prediction K-MEANS improved particle swarm optimization(IPSO) Levenberg-Marquardt(LM) radial basis function(rbf)neural network
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PARAMETERS DETERMINATION METHOD OF PHASE-SPACE RECONSTRUCTION BASED ON DIFFERENTIAL ENTROPY RATIO AND RBF NEURAL NETWORK 被引量:4
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作者 Zhang Shuqing Hu Yongtao +1 位作者 Bao Hongyan Li Xinxin 《Journal of Electronics(China)》 2014年第1期61-67,共7页
Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reco... Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly. 展开更多
关键词 Phase-space reconstruction Chaotic time series Differential entropy ratio Embedding dimension Time delay Radial Basis Function(rbf) neural network
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Nonlinear modeling based on RBF neural networks identification and adaptive fuzzy control of DMFC stack 被引量:1
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作者 苗青 曹广益 朱新坚 《Journal of Shanghai University(English Edition)》 CAS 2006年第4期346-351,共6页
The temperature models of anode and cathode of direct methanol fuel cell (DMFC) stack were established by using radial basis function (RBF) neural networks identification technique to deal with the modeling and co... The temperature models of anode and cathode of direct methanol fuel cell (DMFC) stack were established by using radial basis function (RBF) neural networks identification technique to deal with the modeling and control problem of DMFC stack. An adaptive fuzzy neural networks temperature controller was designed based on the identification models established, and parameters of the controller were regulated by novel back propagation (BP) algorithm. Simulation results show that the RBF neural networks identification modeling method is correct, effective and the models established have good accuracy. Moreover, performance of the adaptive fuzzy neural networks temperature controller designed is superior. 展开更多
关键词 direct methanol fuel cell (DMFC) stack radial basis function rbf neural networks contxoller.
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A nonlinear PCA algorithm based on RBF neural networks 被引量:1
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作者 杨斌 朱仲英 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第1期101-104,共4页
Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal com... Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction. 展开更多
关键词 Principal Component Analysis (PCA) Nonlinear PCA (NLPCA) Radial Basis Function (rbf) neural network Orthogonal Least Squares (OLS)
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Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network 被引量:1
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作者 Guangwu Liu Jing Long +3 位作者 Lingzhi Yang Zhaoyi Su Dechen Yao Xiangli Zhong 《Journal of Intelligent Learning Systems and Applications》 2013年第4期211-215,共5页
This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signa... This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults. 展开更多
关键词 Fault DIAGNOSIS Urban RAIL Vehicle AUXILIARY Inverter WAVELET PACKET rbf neural network
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A Model to Predict Rolling Force of Finishing Stands with RBF Neural Networks
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作者 应宇圣 王景成 陈春召 《Journal of Shanghai Jiaotong university(Science)》 EI 2005年第3期256-259,共4页
In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with tradition... In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models. 展开更多
关键词 radial basis function(rbf neural networks prediction of rolling force finishing rolling
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Multi-Deployment of Dispersed Power Sources Using RBF Neural Network
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作者 Yaser Soliman Qudaih Takashi Hiyama 《Energy and Power Engineering》 2010年第4期213-222,共10页
Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to asses... Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process;so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy;therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method. 展开更多
关键词 Dispersed POWER SOURCES DEPLOYMENT rbf neural network POWER LOSSES Reduction
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A Short-Term Traffic Flow Prediction ModelBased on Quantum Genetic Algorithm andFuzzy RBF Neural Networks
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作者 Kun Zhang 《计算机科学与技术汇刊(中英文版)》 2016年第1期24-39,共16页
关键词 神经网络 流动模拟 基因算法 rbf 交通 预言 短期 ARIMA
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Dynamic modeling and RBF neural network compensation control for space flexible manipulator with an underactuated hand 被引量:1
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作者 Dongyang SHANG Xiaopeng LI +1 位作者 Meng YIN Fanjie LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第3期417-439,共23页
In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter pertur... In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH. 展开更多
关键词 Space flexible manipulator rbf neural network Underactuated hand Dynamic models Model simplification
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