Selforganizing Clifford neural network
Eduardo Bayro Corrochano, Sven Buchholz, Gerald Sommer · 2002
This paper presents a novel self-organizing type RBF neural network and introduces the geometric algebra in the neural computing field. Real valued neural nets for function approximation require feature enhancement, dilation and rotation operations and are limited by the Euclidean metric. This coordinate-free geometric framework allows to process patterns between layers in a particular dimension and desired metric being possible only due to the promising projective split. The potential of such nets working in a Clifford algebra C(V/sub p,q/) is shown by a simple application of frame coordination in robotics.