Neural equalizer with adaptive multidimensional spline activation functions

This paper presents a new neural architecture suitable for digital signal processing application. The architecture, based on adaptable multidimensional activation functions, allows one to collect information from the previous network layer in aggregate form. In other words the number of network connections (structural complexity) can be very low respect to the problem complexity. This fact, as experimentally demonstrated in the paper, improve the network generalization capabilities and speed up the convergence of the learning process. A specific learning algorithm is derived and experimental results, on channel equalization, demonstrate the effectiveness of the proposed architecture.