3.3. Numerical results of parameter recognition using BPANN

The first supervised learning employed for parameter recognition of engineering constants is the BPANN, which can be easily implemented for multiple inputs and multiple outputs. Since there are various design parameters in the construction of a BPANN, we study some influence factors (such as different algorithms of training rules, different combination of input variables, and number of neurons of hidden layer), and then we propose and analyze an appropriate topology of BPANN. The MATLAB nntool is used for network simulation.
