Turbojet modeling in windmilling based on radial basis function networks

被引：0

作者：

Yu, D.R. ^{[1
]}

Guo, Y.F. ^{[1
]}

Niu, J. ^{[1
]}

Shi, X.X. ^{[1
]}

He, B.C. ^{[1
]}

机构：

[1] Sch. of Energy Sci. and Eng., Harbin Inst. of Technol., Harbin 150001, China

来源：

Tuijin Jishu/Journal of Propulsion Technology | 2001年 / 22卷 / 03期

关键词：

Computer simulation - Data processing - Missiles - Neural networks - Radial basis function networks;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The windmilling process of missile turbojet is such a complex nonlinear process that to obtain its dynamic model theoretically is very difficult because the compressor works in expending mode (non-normal operating mode) in this condition. Considering the great capacity of handling nonlinearity of the neural network, an experimental model of the windmilling process using radial basis function networks (RBFN) was established and a good precision through selecting the parameters and the training samples of the network properly was gained. The neural network model is of great value for computing the point of ignition or simulating the windmilling process.

引用

页码：183 / 186

共 50 条

[21] A classification technique based on radial basis function neural networks
Sarimveis, H
Doganis, P
Alexandridis, A
ADVANCES IN ENGINEERING SOFTWARE, 2006, 37 (04) : 218 - 221
[22] Radial Basis Function Networks GPU-Based Implementation
Brandstetter, Andreas
Artusi, Alessandro
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2008, 19 (12): : 2150 - 2154
[23] OFDM channel equalization based on radial basis function networks
Moffa, Giuseppina
ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2, 2006, 4132 : 201 - 210
[24] Radial basis function based meshless method for groundwater modeling
Li, JC
Chen, YT
CURRENT TRENDS IN SCIENTIFIC COMPUTING, 2003, 329 : 237 - +
[25] The Vector Clustering Based on the Recursive Particle Swarm Optimization with Radial Basis Function Networks Modeling System
Jia, Xue-ming
2016 INTERNATIONAL CONFERENCE ON ENVIRONMENT, CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT (ECCSD 2016), 2016, : 298 - 304
[26] Replacement based non-linear data reduction in radial basis function networks QSAR modeling
Malek-Khatabi, Atefe
Kompany-Zareh, Mohsen
Gholami, Somayeh
Bagheri, Saeed
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2014, 135 : 157 - 165
[27] Function emulation using radial basis function networks
Chakravarthy, SV
Ghosh, J
NEURAL NETWORKS, 1997, 10 (03) : 459 - 478
[28] Control for nonlinear chaos based on radial basis function neural networks
Wen, T
Nan, WY
Wu, ZS
Nian, WJ
PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4, 2002, : 1505 - 1508
[29] Vehicle Type Recognition Based On Radial Basis Function Neural Networks
Wang, Weihua
FIRST IITA INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2009, : 444 - 447
[30] Satisfiability of Logic Programming Based on Radial Basis Function Neural Networks
Hamadneh, Nawaf
Sathasivam, Saratha
Tilahun, Surafel Luleseged
Choon, Ong Hong
PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): GERMINATION OF MATHEMATICAL SCIENCES EDUCATION AND RESEARCH TOWARDS GLOBAL SUSTAINABILITY, 2014, 1605 : 547 - 550

← 1 2 3 4 5 →