Multilayer feedforward networks with adaptive spline activation function

In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the ASNN´s high representation capabilities, networks with a small number of interconnections can be trained to solve both pattern recognition and data processing real-time problems. The main idea is to use a Catmull-Rom cubic spline as the neuron´s activation function, which ensures a simple structure suitable for both software and hardware implementation. Experimental results demonstrate improvements in terms of generalization capability and of learning speed in both pattern recognition and data processing tasks