There are well-known limitations and drawbacks on the performance and robustness of the feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs). In this study we shall address them by Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and in this paper we shall show that this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. Experimental results show that POPs can scale up very well with the problem size and can have the potential to achieve a superior generalization performance on real benchmark problems with a significant gain. To address the limitations and drawbacks of MLP neuron model the generalized model of the biological neurons is proposed.Progressive Operational Perceptrons (POPs) is self adaptive and built progressively (incrementally) just like biological neurons.A POP shares the same properties of a typical MLPs and can be identical to a MLP providing that the MLP operators are used.The best set of operators is searched according to the learning problem at hand and the minimal network is built progressively.With the right blend of non-linear operators, POPs can learn very complex problems that cannot be learnt by deeper MLPs.
['Serkan Kiranyaz', 'Turker Ince', 'Alexandros Iosifidis', 'Moncef Gabbouj']
Progressive Operational Perceptrons