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Image Reconstruction by the Complex-Valued Neural Networks: Design by Using Generalized Projection Rule

Image Reconstruction by the Complex-Valued Neural Networks: Design by Using Generalized Projection Rule
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Author(s): Donq-Liang Lee (Ming-Chuan University, Taiwan)
Copyright: 2009
Pages: 20
Source title: Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters
Source Author(s)/Editor(s): Tohru Nitta (National Institute of Advanced Industrial Science and Technology, Japan)
DOI: 10.4018/978-1-60566-214-5.ch010

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Abstract

New design methods for the complex-valued multistate Hopfield associative memories (CVHAMs) are presented. The author of this chapter shows that the well-known projection rule can be generalized to complex domain such that the weight matrix of the CVHAM can be designed by using the generalized inverse technique. The stability of the presented CVHAM is analyzed by using energy function approach which shows that in synchronous update mode a CVHAM is guaranteed to converge to a fixed point from any given initial state. Moreover, the projection geometry of the generalized projection rule is discussed. In order to enhance the recall capability, a strategy of eliminating the spurious memories is reported. Next, a generalized intraconnected bidirectional associative memory (GIBAM) is introduced. A GIBAM is a complex generalization of the intraconnected BAM (IBAM). Lee shows that the design of the GIBAM can also be accomplished by using the generalized inverse technique. Finally, the validity and the performance of the introduced methods are investigated by computer simulation.

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