Adaptive multidimensional spline neural network for digital equalization

This paper presents a new neural architecture suitable for digital signal processing applications. The architecture, based on adaptable multi-dimensional activation functions, allows 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.