On Characterization of Attractor Basins of Fuzzy Multiple Attractor Cellular Automata

Authors:
Pradipta Maji
Affiliations:
(Correspd.) Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India. E-mail: pmaji@isical.ac.in
Venue:
Fundamenta Informaticae
Year:
2008

Citing 15
Cited 0

Neural network models for pattern recognition and associative memory

Neural Networks
Transition phenomena is cellular automata rule space

Physica D
Introduction to the theory of neural computation

Introduction to the theory of neural computation
Cellular automata in pattern recognition

Information Sciences: an International Journal
Hierarchy of fuzzy cellular automata

Fuzzy Sets and Systems
Cellular automata in fuzzy backgrounds

Physica D
CA-like error propagation in fuzzy CA

Parallel Computing - Special issue: cellular automata
Convergence and aperiodicity in fuzzy cellular automata: revisiting rule 90

Physica D
Highly regular, modular, and cascadable design of cellular automata-based pattern classifier

IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on system-level interconnect prediction
Evolving Cellular Automata as Pattern Classifier

ACRI '01 Proceedings of the 5th International Conference on Cellular Automata for Research and Industry
Theory and application of cellular automata for pattern classification

Fundamenta Informaticae - Special issue on cellular automata
Fuzzy Cellular Automata for Modeling Pattern Classifier

IEICE - Transactions on Information and Systems
Design and characterization of cellular automata based associative memory for pattern recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Error correcting capability of cellular automata based associative memory

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata

Fundamenta Informaticae - Special issue on DLT'04

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two new operators, namely, dependency vector (DV) and derived complement vector (DCV) are introduced in this paper to characterize the attractor basins of the additive fuzzy cellular automata (FCA) based associative memory, termed as fuzzy multiple attractor cellular automata (FMACA). The introduction of DV and DCV makes the complexity of the attractor basin identification algorithm linear in time. The characterization of the FMACA using DV and DCV establishes the fact that the FMACA provides both equal and unequal size of attractor basins. Finally, a set of algorithms is proposed to synthesize the FCA rules, attractors, and predecessors of attractors from the given DV and DCV in linear time complexity.