A new geometric recurrent neural network based on radial basis function and Elman models

In this paper we present a new hypercomplex-valued model of recurrent neural network which is based on the Geometric Radial Basis (RBF) and Elman Network Models. This model is useful to recognize temporal sequences of geometric entities using geometric algebra. Our model combines features from the Elman recurrent neural network and geometric RBF networks. This network constitutes a generalization of the standard real-valued recurrent models. The network fed with geometric entities can be used in real time to learn a sequence of entities determined using a geometric language. This approach calculates the temporal geometric transformation between each two entity orientations which are presented to the network in different times.