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.
Guarnieri
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Dipartimento di Elettronica e Automatica, Università di Ancona, Italy, 60131 Ancona, Italy.
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