Rotational quadratic function neural networks

Abstract

The authors present a novel architecture, known as the rotational quadratic function neuron (RQFN), to implement the quadratic function neuron (QFN). Although with some loss in the degree of freedom in the boundary formation, RQFN possesses some attributes which are unique when compared to QFN. In particular, the architecture of RQFN is modular, which facilitates VLSI implementation. Moreover, by replacing QFN by RQFN in a multilayer perceptron (MP), the fan-in and the interconnection volume are reduced to that of MP utilizing linear neurons. In terms of learning, RQFN also offers varieties such as the separate learning paradigm and the constrained learning paradigm. Single-layer MP utilizing RQFNs have been demonstrated to form more desirable boundaries than the normal MP. This is essential in the scenario where either the closure of the boundary or boundaries of higher orders are required.<>