摘要：In this study, a novel algorithm to estimate the optimum value of the fuselage drag coefficient is designed by integrating the artificial neural network (ANN) which is an artificial intelligent method into the algorithm of simultaneous perturbation stochastic approximation (SPSA) which is a fast method. SPSA converges to the optimum value for solution very fast. However using SPSA alone requires a function of problem to estimate the optimum solution. On the other hand, ANN is able to estimate the solutions for the problem without need of its any objective function. However ANN needs a certain data set to be effectively trained. Also, the best ANN architecture which accomplish with different data sets of problem may alter. Thus, ANN architecture alone is not adequate for estimating the best result for each different data set. The main target of this study is making SPSA able to be applicable for the problem that has not any objective function by using training capability of ANN. For this purpose, initially, ANN is trained by the data of fuselage drag coefficient obtained by previous experimental results conducted in wind tunnel and varies depending on the geometry of fuselage. Thus, ANN becomes capable to estimate the fuselage drag coefficient for each parameter values of the fuselage shape. Therefore, ANN estimates the fuselage drag coefficient with respect to inputs without the requirement of any experimental computations. Note that ANN does not estimate the optimum value as output but estimates the output regarding to the inputs. The ANN is integrated into the SPSA to fulfill the need of cost function for SPSA. More clearly, the new algorithm evaluates ANN to estimate the fuselage drag coefficient with respect to inputs while evaluates SPSA to estimate the optimum inputs for the optimum fuselage drag coefficient. Through integrating the trained ANN into the SPSA, an effective and novel algorithm estimates the fuselage drag coefficient fast and accurately without defining an objective function is improved.
其他摘要：Bu çalışmada gövde sürükleme katsayısının en uygun değerini hesaplamak için yapay zeki bir yöntem olan Yapay Sinir Ağları (YSA), hızlı bir yöntem olan Eşzamanlı Dağılım Rassal Yaklaşım (EDRY) algoritması içerisine yerleştirilerek yeni bir algoritma tasarl
