Linear Analysis Of Telecommunication Tower Systems Using Artificial Intelligence

Training or learning algorithms in Artificial Neural Networks ( ANNs) have been successfully applied in telecommunication towers to calculate accurately their natural frequency in different supporting conditions. One of the most important training and learning algorithm is back propagation algorithm .It is the most used training algorithm for feed forward artificial neural networks. It is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, using SAP۲۰۰۰ program, the real frequency is calculated and is defined as a goal function for neural network, so that all outputs of the network can be compared to this function and the error can be calculated. After that, the MATLAB software package was used to create the appropriate neural networks for aset of inputs including dimensions or specifications of telecommunication towers. According to results, it is concluded that the performance of the neural network is optimum, and the errors are less than ۵%, so the network can perform training in different manner. Furthermore, compare with analysis time of SAP۲۰۰۰ software, the time of frequency calculations in neural network is very low and its precision is acceptable(less than ۹%).

کلیدواژه ها:

natural frequency ، artificial intelligence ، telecommunication towers ، training and learning algorithm.