AN IMPROVED NEURAL NETWORK MODEL FOR NONLINEAR AEROELASTIC ANALYSIS

An improved neural network-based method for predicting nonlinear oscillations in the aeroelastic response is presented. An articial neural network is trained using the limited available information of a short transient data set, and the asymptotic state of the signal is reconstructed by a multi-step (or recursive) prediction process. An enhanced two-layer feedforward neural network with features that control the propagation of the prediction errors is designed. Methods for consistently choosing the number of network inputs and of neurons in the hidden layer for a given application are reported. The proposed predictor has been applied to wind-tunnel experimental data that model an oscillating airfoil with polynomial restoring forces, as well as to signals generated numerically by solving a dieren tial system that models a self-excited two-degree-of-freedom airfoil oscillating in pitch and plunge.

[1]  Charles M. Denegri,et al.  Limit Cycle Oscillation Prediction Using Artificial Neural Networks , 2001 .

[2]  Shih-Ming Yang,et al.  Structural damage identification using pole/zero dynamics in neural networks , 2001 .

[3]  Charles M. Denegri,et al.  Limit Cycle Oscillation Flight Test Results of a Fighter with External Stores , 2000 .

[4]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  A. Kurdila,et al.  Adaptive Feedback Linearization for the Control of a Typical Wing Section with Structural Nonlinearity , 1997, 4th International Symposium on Fluid-Structure Interactions, Aeroelasticity, Flow-Induced Vibration and Noise: Volume III.

[7]  Narasimhan Sundararajan,et al.  Stable Neuro-Flight-Controller Using Fully Tuned Radial Basis Function Neural Networks , 2001 .

[8]  S. J. Price,et al.  NONLINEAR AEROELASTIC ANALYSIS OF AIRFOILS : BIFURCATION AND CHAOS , 1999 .

[9]  A. Kurdila,et al.  Stability and Control of a Structurally Nonlinear Aeroelastic System , 1998 .

[10]  Ivan Catton,et al.  Fault Tolerance and Extrapolation Stability of a Neural Network Air-Data Estimator , 1999 .

[11]  Nateri K. Madavan,et al.  AERODYNAMIC DESIGN USING NEURAL NETWORKS , 2000 .

[12]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[13]  Yau Shu Wong,et al.  Neural Network Approach for Nonlinear Aeroelastic Analysis , 2003 .