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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1795-1803页

作者： Ziqi Zhang Zhong Chen Qi Zhao Yi Wang Jiang Tian School of Electrical Engineering Southeast UniversityNanjingChina Suzhou Power Supply Branch of the State Grid Jiangsu Electric Power Company SuzhouChina NARI Group Corporation Ltd. (State Grid Electric Power Research Institute Ltd.)NanjingChina

The significance of situation awareness(SA) in power systems has increased to enhance the utilization of gridconnected renewable energy power generation(REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization(RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network(RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm(AFSA), enabling the neural network(NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol' method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

关键词： Renewable energy radial basis function neural network robust optimization sensitivity analysis situation awareness

Application of Near Infrared Diffuse Reflectance Spectroscopy with radial basis function neural network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

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Chemical Research in Chinese Universities 2007年第5期23卷 518-523页

作者： DU Lin-na WU Li-hang LU Jia-hui GUO Wei-liang MENG Qing-fan JIANG Chao-jun SHEN Si-le TENG Li-rong College of Life Science College of Animal Science and Veterinary Medicine College of Chemistry Jilin University Changchun 130012 P. R. China

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide ***-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance *** raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD *** component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input *** optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation *** RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,*** these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,*** results are better than those obtained from PLS models and BPNN *** additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

关键词： Rifampicin isoniazid and pyrazinamide tablets NIR diffuse reflectance spectroscopy Partial least square Back-propagation neural network radial basis function neural network

High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

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Geoscience Frontiers 2022年第1期13卷 364-373页

作者： Guocheng Hao Juan Guo Wei Zhang Yunliang Chen David AYuen School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Department of Mathematics Duke UniversityDurhamNC 27708USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY 10027USA

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and *** change tendency may be related to the occurrence of earthquake *** of the underlying intensity trend plays an important role in the analysis of data and disaster *** chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field *** main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field *** verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data *** to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function ***,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.

关键词： Earth’s natural pulse electromagnetic field Chaos theory radial basis function neural network Forecasting model

Wear State Recognition of Drills Based on K-means Cluster and radial basis function neural network

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International Journal of Automation and computing 2010年第3期7卷 271-276页

作者： Xu Yang Department of Mechanical System Engineering Faculty of Engineering Gunma University Kiryu 376-8515 Japan Department of Mechanical Engineering School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian 116034 PRO

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.

关键词： Drill wear state recognition cutting torque signals wavelet packet decomposition (WPD) Welch spectrum energy K-means cluster radial basis function neural network

A Novel radial basis function neural network Approach for ECG Signal Classification

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Intelligent Automation & Soft Computing 2023年第1期35卷 129-148页

作者： S.Sathishkumar R.Devi Priya Department of Information Technology Adhiyamaan College of EngineeringHosur621004India Department of Information Technology Kongu Engineering CollegeErode638060India

ions in the ECG *** cardiologist and medical specialistfind numerous difficulties in the process of traditional *** specified restrictions are eliminated in the proposed *** fundamental aim of this work is tofind the R-R *** analyze the blockage,different approaches are implemented,which make the computation as facile with high *** information are recovered from the MIT-BIH *** retrieved data contain normal and pathological ECG *** obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is *** amplitude is computed to detect the cardiac *** R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is *** Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the *** addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target ***,the RBFNN(radial basis function neural network)is applied,which diminishes the local minima present in the *** shows enhancement in characterizing the ordinary and anomalous ECG signals.

关键词： Electrocardiogram signal gaborfilter discrete cosine based discrete orthogonal stock well transform genetic algorithm radial basis function neural network

A prediction method for the wax deposition rate based on a radial basis function neural network

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Petroleum 2017年第2期3卷 237-241页

作者： Ying Xie Yu Xing School of Petroleum Engineering Southwest Petroleum UniversityChengdu610500China

The radial basis function neural network is a popular supervised learning tool based on machinery learning *** high precision having been proven,the radial basis function neural network has been applied in many *** accumulation of deposited materials in the pipeline may lead to the need for increased pumping power,a decreased flow rate or even to the total blockage of the line,with losses of production and capital investment,so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil *** paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors,the pipe wall temperature gradient,pipe wall wax crystal solubility coefficient,pipe wall shear stress and crude oil viscosity,by the gray correlational analysis *** software is employed to establish the RBF neural *** with the previous literature,favorable consistency exists between the predicted outcomes and the experimental results,with a relative error of 1.5%.It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

关键词： Crude oil Prediction model radial basis function neural network Wax deposition rate

An Improved Particle Swarm Optimization Algorithm for radial basis function neural network

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An Improved Particle Swarm Optimization Algorithm for Radial...

Frontier-based Exploration on Continuous radial basis function neural network Map

2009中国控制与决策会议

作者： Duan Qichang~1,Zhao Min~1,Duan Pan~2 1.College of Automation,Chong qing University,Chong qing,400044 2.College of Electrical Engineering,Chong qing University,Chong qing,400044

＜正＞An improved particle swarm optimization（IMPSO） which synthesizes the existing models of constriction factor approach（CFA PSO） is *** the proposed method,an adaptive algorithm based on the search space adjustable is applied to solve the problem that conventional particle swarm optimization（PSO） algorithm easily falls into local optimal and occur premature ***,the IMPSO is used to optimize the parameters of RBF neural *** new training algorithm is used to approximate polynomial function and predict chaotic time series, compared with PSO,and CFA PSO,the algorithm speed up the speed of convergence,and has much greater accuracy.

关键词： particle swarm optimization radial basis function neural network nearest neighbor cluster algorithm constriction factor

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Frontier-based Exploration on Continuous Radial Basis Functi...

Prediction of reservoir brine properties using radial basis function (RBF) neural network

第37届中国控制会议

作者： Yuansong Hou Xiaogang Ruan Xiaoqing Zhu Cheng Li College of Electronic and Control Engineering Beijing University of Technology

We present a frontier-driven autonomous robotic exploration method on a continuous representation of *** approach utilizes radial basis function neural network to build continuous occupancy grid *** frontiers are calculated directly by gradient field of occupancy probability distribution,which clear show division between free and unexplored ***,the resulting frontiers provide a measure of quality *** is present to show the performance of the proposed technique.

关键词： Mobile Robot Frontier-Based Exploration radial basis function neural network

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Petroleum 2015年第4期1卷 349-357页

作者： Afshin Tatar Saeid Naseri Nick Sirach Moonyong Lee Alireza Bahadori oung Researchers and Elite Club North Tehran BranchIslamic Azad UniversityTehranIran Department of Petroleum Engineering Ahwaz Faculty of Petroleum Engineering Petroleum University of TechnologyAhwazIran Southern Cross University School of EnvironmentScience and EngineeringPO Box 157LismoreNSWAustralia School of Chemical Engineering Yeungnam UniversityGyeungsanRepublic of Korea

Aquifers,which play a prominent role as an effective tool to recover hydrocarbon from reservoirs,assist the production of hydrocarbon in various *** so-called water flooding methods,the pressure of the reservoir is intensified by the injection of water into the formation,increasing the capacity of the reservoir to allow for more hydrocarbon *** studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding ***,various characteristics of brines are required for different calculations used within the petroleum ***,it is of great significance to acquire the exact information about PVT properties of brine extracted from *** properties of brine that are of great importance are density,enthalpy,and vapor *** this study,radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned *** root mean squared error of 0.270810,0.455726,and 1.264687 were obtained for reservoir brine density,enthalpy,and vapor pressure,*** predicted values obtained by the proposed models were in great agreement with experimental *** addition,a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.

关键词： Reservoir brine Intelligent method Density Enthalpy Vapor pressure radial basis function neural network

A Simple Hybrid Recursive Learning Algorithm with High Generalization Performance for radial basis function neural network

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Systems Science and Systems Engineering 2000年第1期10卷 16-27页

作者： ZHU Tao,\ WANG Zheng\|ou Institute of Systems Engineering, Tianjin University, Tianjin 300072, China

In this paper, we propose a simple learning algorithm for non\|linear function approximation and system modeling using minimal radial basis function neural network with high generalization performance. A hybrid algorithm is constructed, which combines recursive n \|means clustering algorithm with a simple recursive regularized least squares algorithm (SRRLS). The n \|means clustering algorithm adjusts the centers of the network, while the SRRLS constructs a parsimonious network which makes the generalization performance of the network well. The SRRLS algorithm needs no matrix computing, so it has a lower computational cost and no ill\|conditional problem. Because of the recursive manner, this algorithm is suitable for on\|line applications. The effectiveness of this algorithm is demonstrated by two benchmark examples.

关键词： radial basis function neural network generalization regularized least squares simplicity n\| means clustering recursive algorithm