Design and analysis of multi-valued auto-associative quaternion-valued recurrent neural networks based on external inputs

Abstract

This paper presents a design procedure for synthesizing auto-associative memories of quaternion-valued recurrent neural networks (QVRNNs) based on external inputs. By virtue of the geometrical properties of the activation function and the fixed point theorem, several inequalities are given to guarantee the global exponential stability for the QVRNNs. The proposed QVRNNs are robust in terms of the design parameter selection and neurons are reduced. Several illustrative examples applied to true color images are given to guarantee the validity of the results.