Abstract
In this brief, threshold complex-valued neural associative memory is proposed for information retrieval. The introduction of threshold improves network performance by excluding rotated patterns from spurious memories. A design method for constructing different types of network is developed based on complex matrix decomposition, which is capable of designing nonthreshold, threshold, non-Hermitian, and Hermitian networks. Further, we illustrate the performance of the proposed method by reconstructing noisy 256 grayscale and true color images. The results show that constructed networks can work efficiently, threshold networks have better performance than nonthreshold ones and networks with small asymmetry in weight matrix function as well as Hermitian ones.
