REGRESSION BASED NEURAL NETWORK FOR STUDYING THE VIBRATION CONTROL OF THE ROTOR BLADE FOR MICRO-UNMANNED HELICOPTER
The aim of this paper is to demonstrate the use of Regression Based Neural Network (RBNN) method to study the problem of the natural frequencies of the rotor blade for micro unmanned helicopter [2].The training of the traditional ANN (Artificial Neural Network) model and proposed RBNN model has been implemented in the MATLAB environment using NNT (Neural Network Tools) built-in functions.The natural frequencies (Omega) of the blade for the helicopter graphs are plotted for estimation of the natural frequencies (fl, f2, f3).The results obtained in this research show that the RBNN model, when trained, can give the vibration frequency parameters directly without going through traditional and lengthy numerical solutions procedures.Succeeding this, the numerical results, when plotted, show that with the increase in Omega, there is increase in lagging motion frequencies.The increase in the lower mode natural frequencies is smaller than that of the higher modes.This finding is in agreement with the results reported in earlier research [2],[3],[4] carried out by employing Rayleigh-Ritz and FEM respectively.
Transverse vibrations artificial neural network harmonic motion mean square error
ATMA SAHU S.CHAKRAVERTY
Coppin State University,Baltimore MD 21216,USA National Institute of Technology,Rourkela - 769 008,Orissa
国际会议
3rd International Conference on Mechanical and Electrical Technology(ICMET2011) (2011第三届机械与电气技术国际会议)
大连
英文
189-194
2011-08-26（万方平台首次上网日期，不代表论文的发表时间）