In the present paper, a new synthesis approach is developed for associate memories based on a modified relaxation algorithm. The design problem, of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of pseudo relaxation method is evident. The pseudo relaxation training in the synthesis algorithms is guaranteed to converge for the design of neural networks without any constraints on the connection matrix. To demonstrate the applicability of the present result and to compare the present synthesis approach with existing design methods, a pattern recognition example is considered.
In fractal image compression an image is coded as a set of contractive transformations, and is guaranteed to generate an approximation to the original image when iteratively applied to any initial image. In this paper we present a method for mapping similar regions within an image by an approximation of the collage error; that is, range blocks can be approximated by a linear combination of domain blocks.