Increased emphasis on rotorcraft performance and operational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. The effect of presence of outliers radial basis function neural network pdf the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data.

Check if you have access through your login credentials or your institution. Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. Then, NRBF has been used to determine the magnetic field distribution in a new geometry differing from the geometries used for training. These test results show that proposed NRBF network can be used as useful tool to calculate the magnetic fields from power lines, alternative to the conventional methods.

We construct the radial basis neural network models to predict the magnetic field of power lines. Data for training the neural network is obtained from the numerical simulations. The developed NRBF model makes it possible to determine the magnetic fields easier. NRBF model provides considerable reduction of analysis time. The proposed method ensures accept able accuracy and satisfying convergence. This article is about the computer algorithm.

The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a case propagates through the network. Two assumptions must be made about the form of the error function. The reason for this assumption is that the backpropagation algorithm calculates the gradient of the error function for a single training example, which needs to be generalized to the overall error function.

The second assumption is that it can be written as a function of the outputs from the neural network. The optimization algorithm repeats a two phase cycle, propagation and weight update. When an input vector is presented to the network, it is propagated forward through the network, layer by layer, until it reaches the output layer. The resulting error value is calculated for each of the neurons in the output layer. The error values are then propagated from the output back through the network, until each neuron has an associated error value that reflects its contribution to the original output. Backpropagation uses these error values to calculate the gradient of the loss function.