Title :
A class of discrete-time recurrent neural networks with multivalued neurons
Author :
Zhou, Wei ; Zurada, Jacek M.
Author_Institution :
Comput. Intell. Lab., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
This paper discusses a class of discrete time recurrent neural networks with multivalued neurons (MVN) with complex-valued weights and an activation function defined as a function of the argument of a weighted sum. Complementing state-of-the-art of such networks, this paper focuses on the convergence analysis of such networks in synchronous update mode. One theorem is presented and simulation results are used to illustrate the theory.
Keywords :
discrete time systems; recurrent neural nets; activation function; complex-valued weight; convergence analysis; discrete-time recurrent neural network; multivalued neuron; synchronous update mode; weighted sum; Associative memory; Computational intelligence; Convergence; Helium; Laboratories; Neural networks; Neurons; Recurrent neural networks; Symmetric matrices; USA Councils;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178721
