<title>Invariant pattern recognition using higher-order neural networks</title>

Abstract

The paper provides a discussion of the results derived from the theory of invariant higher- order neural networks to design a system which will produce an invariant classification solution for a particular pattern recognition problem. This is done by employing a generalized to higher-orders back-propagation algorithm with reduced network connectivity. In special case optimal solution is obtained using linear equation technique. In both cases the volume of computations in the algorithm is much less, than that of the other methods. We demonstrate that the system can correctly classify shifted, rotated, scaled and distorted patterns with a certain amount of noise.