Title :
Improvements of Complex-Valued Hopfield Associative Memory by Using Generalized Projection Rules
Author_Institution :
Dept. of Inf. & Telecommun. Eng., Ming Chuan Univ., Taoyuan
Abstract :
In this letter, new design methods for the complex-valued multistate Hopfield associative memories (CVHAMs) are presented. We show that the well-known projection rule proposed by Personnaz can be generalized to complex domain such that the weight matrix of the CVHAM can be designed by using a simple and effective method. The stability of the proposed CVHAM is analyzed by using energy function approach which shows that in synchronous update mode the proposed model is guaranteed to converge to a fixed point from any given initial state. Moreover, the projection geometry of the generalized projection rule (GPR) is discussed. In order to enhance the recall capability, a strategy of eliminating the spurious memories is also reported. The validity and the performance of the proposed methods are investigated by computer simulation
Keywords :
Hopfield neural nets; content-addressable storage; stability; complex-valued multistate Hopfield associative memories; energy function method; generalized projection rules; projection geometry; stability; Associative memory; Computer simulation; Design methodology; Geometry; Ground penetrating radar; Neural networks; Neurons; Prototypes; Quantization; Stability analysis; Complex-valued Hopfield associative memory (CVHAM); generalized projection rule (GPR); spurious memory; Algorithms; Artificial Intelligence; Cluster Analysis; Computing Methodologies; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.878786