A Neural Network Prototype for Predicting F-14B Strains at the B.L. 10 Longeron
Final rept. Mar-May 1992
NAVAL AIR WARFARE CENTER AIRCRAFT DIV WARMINSTER PA AIR VEHICLE AND CREW SYSTEMS TECHNOLOGY DEPT
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A neural network prototype was developed to predict strain from data obtained from an F-14 flight test program. Data from two flights were available Flight 400 consisted of standard structural maneuvers, and Flight 401 consisted of maneuvers performed during typical fleet operations. Several variables were monitored during flight, including Nz, Mach number, altitude, wingsweep angle, roll rate, angle of attack, a weight-on-wheels indicator and the strain at B.L. 10 of the F-14B. The neural network was trained on Flight 400, and tested on Flight 401. A forward-stepwise-regression was also performed on Flight 400 and the selected model was tested on Flight 401, for comparison. Results were evaluated by comparing the correlation coefficients between the predicted and measured strains. The correlation coefficient obtained by the neural network was 0.93 and by the regression equation was 0.94. Based on these preliminary results, the conclusion is made that the neural network approach offers a viable alternative to standard regression analysis for predicting strains on airframes.
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