Title :
Hetero-associative memories via globally asymptotically stable discrete-time cellular neural networks
Author :
Grassi, Giuseppe ; Acciani, Giuseppe
Author_Institution :
Dipt. di Ingegneria dell´´Innovazione, Lecce Univ., Italy
Abstract :
In this paper hetero-associative memories are designed using globally asymptotically stable discrete-time cellular neural networks (DTCNNs). The approach, which assures the global asymptotic stability of the equilibrium point by exploiting circulant matrices in the design phase, generates networks where the input data are fed via external inputs rather than initial conditions. This feature makes it possible to implement hetero-associative memories via DTCNNs running in real time
Keywords :
asymptotic stability; cellular neural nets; content-addressable storage; real-time systems; asymptotic stability; circulant matrices; discrete-time cellular neural networks; equilibrium point; hetero-associative memory; real time systems; Associative memory; Asymptotic stability; Cellular neural networks; Cloning; Difference equations; Discrete Fourier transforms; Feedback; Frequency domain analysis; Steady-state; Very large scale integration;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location :
Catania
Print_ISBN :
0-7803-6344-2
DOI :
10.1109/CNNA.2000.876835
