Generation of TPMACA for Pattern Classification

Autor:	Jin-Gyoung Kim, Han-Doo Kim, Gil-Tak Gong, Un-Sook Choi, Sook-Hee Kwon, Sung-Jin Cho, Seok-Tae Kim
Rok vydání:	2014
Předmět:	State-transition matrix Very-large-scale integration Computer science business.industry Attractor Pattern recognition Artificial intelligence Modular design business Classifier (UML) Cellular automaton
Zdroj:	Lecture Notes in Computer Science ISBN: 9783319115191 ACRI
DOI:	10.1007/978-3-319-11520-7_42
Popis:	The important prerequisites of designing pattern classifier are high throughput and low cost hardware implementation. The simple, regular, modular and cascadable local neighborhood sparse network of Cellular Automata (CA) suits ideally for low cost VLSI implementation. Thus the multiple attractor CA is adapted for use as a pattern classifier. By concatenating two predecessor multiple attractor CA (TPMACA) we can construct a pattern classifier. In this paper we propose a method for finding dependency vector by using a 0-basic path. Also we propose various methods for generating TPMACA corresponding to a given dependency vector.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d842c4cbda8e7e72332d4bb33ee60dba https://doi.org/10.1007/978-3-319-11520-7_42 Zobrazit plný text záznamu