In this paper a Cellular Fuzzy Associative Memory containing fuzzy rules for gray image fuzzification is designed, considered as a subsystem of a CNN-based architecture able to store bidimensional patterns. After establishing the fuzzy rules which characterize the required FAM, these rules are properly codified and stored in a Cellular Nonlinear Network behaving as a memory. A numerical example concerning with the stereoscopic vision of a mobile robot is reported to show how the synthesized memory can process bidimensional patterns for robotic vision purposes.

Synthesis of a Cellular Nonlinear Network for a Fuzzy Associative Memory in Stereoscopic Vision

Carnimeo, L.;Giaquinto, A.
2002

Abstract

In this paper a Cellular Fuzzy Associative Memory containing fuzzy rules for gray image fuzzification is designed, considered as a subsystem of a CNN-based architecture able to store bidimensional patterns. After establishing the fuzzy rules which characterize the required FAM, these rules are properly codified and stored in a Cellular Nonlinear Network behaving as a memory. A numerical example concerning with the stereoscopic vision of a mobile robot is reported to show how the synthesized memory can process bidimensional patterns for robotic vision purposes.
Advances in systems engineering, signal processing and communications
960-8052-696
World Scientific Engineering Society Press
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/13383
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