A variant of second-order multilayer perceptron and its application to function approximations

A second-order multilayer perceptron that uses a different activation function, the quadratic sigmoid function, is proposed. Unlike the conventional sigmoid activation function, the quadratic sigmoid function exhibits second-order characteristics among the input components. Based on this new activation function, a learning algorithm is developed for the new multilayer perceptron. The proposed multilayer perceptron has been used to approximate continuous-valued functions. The approximation results show that the learning speed and the network size were significantly improved in comparison with the conventional multilayer perceptrons which use the sigmoid activation functions.<
>