Adaptive Multidimensional Spline Neural Network for Digital Equalization

Presents a new neural architecture that is suitable for digital signal processing applications. The architecture, which is 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 (the structural complexity) can be very low with respect to the problem complexity. This fact, as experimentally demonstrated in this paper, improves the network's generalization capabilities and speeds 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.