Hidden nodes of neural network: Useful application in traffic sign recognition

In this paper we presented a technique to extend the supervised feed forward back propagation neural network that we have previously implemented using out-of-plane traffic sign recognition. In this method we have designed a method to find the optimal number of input nodes together with the hidden nodes to acquire the best system performance. This method not only is able to present the proper combinations between the number of input nodes and hidden layers, but also it can train the network to the optimum stage in the shortest possible time. The result is later plotted and the values of input classes and hidden nodes that give the MSE less that 0.01 is found to be 12 and 55, respectively.