Different Hybrid Neural Network in Inverse Design

K.Thinakaran, Dr.R.Rajasekar

Here, we investigate a different hybrid neural network method for the design of airfoil using inverse procedure. The aerodynamic force coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A feedforward neural network is created with input as a aerodynamic coefficient and the output as the airfoil coordinates. In existing algorithm as an FNN training method has some limitation associated with local optimum and oscillation. The cost terms of the first algorithm are selected based on the activation functions of the hidden neurons and first order derivatives of the activation functions of the output neurons. The cost terms of the second algorithm are selected based on the first order derivatives of the activation functions of the hidden neurons and the activation functions of the output neurons. Results indicate that optimally trained artificial neural networks may accurately predict airfoil profile.