جستجوی مقالات مرتبط با کلیدواژه « شبکه های عصبی مصنوعی » در نشریات گروه « میان رشته ای »

In this article, a new closed-loop algorithm is presented to generate an optimal angular trajectory for a given satellite to reach the desired final point. Using the capabilities of artificial neural networks, this algorithm can find the best trajectory to reach the final setpoints based on the dynamic behavior of the system and the preset controller capability by using the desired final values of the trajectory and the values of the system state variables at each simulation time. In the presence of external disturbances, this closed-loop intelligent trajectory generation algorithm shows advanced adaptive performance, which allows it to develop the best alternative trajectory to achieve the final setpoint and return the system to the main trajectory. Despite the fact that this algorithm is able to restore the main trajectory, it is also capable of preventing unreasonable control efforts by considering the control properties of the system. This intelligent algorithm of angular path generation shows high accuracy and effective performance after simulations are performed in the MATLAB software environment with predefined external disturbances.

In this research, a method based on the the artificial neural network is used to determine the location and orientation of cracks in pipes. For this purpose, first, the finite element method is used to model wave propagation and crack modeling in different locations and orientations. In this regard, two types of longitudinal and torsional guided waves are used to excite the structure. The obtained signals are processed in order to calculate the appropriate characteristics. To this end, the reflection echoes are also measured and five features are extracted at six levels from discrete wavelet decomposition of raw signals. Selected properties of the signals are processed to limit the size of the neural network model without losing information. To do so, the firefly algorithm method was used and fed to an artificial neural network that predicts the location and orientation of the crack. In this study, conventional multilayer perceptron diffusion networks have been used. According to the obtained results, it is observed that the proposed method shows good accuracy in predicting the location and orientation of the crack. Also, the percentage of neural network errors is less than 7%.