Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected.Their network structure in which contains the direct links between inp...Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected.Their network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural networks.In this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training RVFL.The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is analyzed.The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.展开更多
The traditional testing method of a hydraulic pump requires a large amount of test data from a variety of pump working conditions. The test is usually time-consuming and energy-consu- ming. And the accurate characteri...The traditional testing method of a hydraulic pump requires a large amount of test data from a variety of pump working conditions. The test is usually time-consuming and energy-consu- ming. And the accurate characteristic curves of the pump were hardly obtained due to a limited a- mount and discreteness of the test data. In order to simplify the test procedure and improve the test accuracy, a novel method for measuring hydraulic pump operating characteristic based on multi-ele- ment nonlinear regression （NLMR） modeling is proposed in this paper. The main idea of this model- ing method is establishing a mathematical model to predict the performance parameters of the hy- draulic pump, only a small amount of test data is needed. Consequently, the pump operating charac- teristics in any working conditions are obtained. And the test results of the pump are easily charac- terized in the graphs, charts, tables and so on. The evaluations of the model are carried out and dis- cussed in this paper. The result shows that the test error of the novel method can be controlled to be about 0. 1%. Compared with the traditional test method, the proposed method reduces greatly the test time and the random error of the test data, and improves the efficiency and accuracy of the pump test.展开更多
基金supported by the Ministry of Science and Technology of China(2018AAA0101000,2017YFF0205306,WQ20141100198)the National Natural Science Foundation of China(91648117)。
文摘Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected.Their network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural networks.In this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training RVFL.The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is analyzed.The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.
基金Supported by the National Science & Technology Pillar Program During the 12th Five-Year Plan Period(2014BAF08B06)the Research Foundation of Beijing Institute of Technology20110601061)
文摘The traditional testing method of a hydraulic pump requires a large amount of test data from a variety of pump working conditions. The test is usually time-consuming and energy-consu- ming. And the accurate characteristic curves of the pump were hardly obtained due to a limited a- mount and discreteness of the test data. In order to simplify the test procedure and improve the test accuracy, a novel method for measuring hydraulic pump operating characteristic based on multi-ele- ment nonlinear regression （NLMR） modeling is proposed in this paper. The main idea of this model- ing method is establishing a mathematical model to predict the performance parameters of the hy- draulic pump, only a small amount of test data is needed. Consequently, the pump operating charac- teristics in any working conditions are obtained. And the test results of the pump are easily charac- terized in the graphs, charts, tables and so on. The evaluations of the model are carried out and dis- cussed in this paper. The result shows that the test error of the novel method can be controlled to be about 0. 1%. Compared with the traditional test method, the proposed method reduces greatly the test time and the random error of the test data, and improves the efficiency and accuracy of the pump test.
