Open AccessA Fast-Efficient Multi Class Pattern Recognition Method
Author(s) -
Rasiq S. M M.Sc.*,
S. Krishnakumar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2893.1081219
Subject(s) - block (permutation group theory) , artificial intelligence , computer science , pattern recognition (psychology) , feature (linguistics) , artificial neural network , variable (mathematics) , class (philosophy) , set (abstract data type) , feature vector , range (aeronautics) , algorithm , mathematics , mathematical analysis , linguistics , philosophy , materials science , geometry , composite material , programming language
This work presents a novel method for multi class pattern recognition. The feature space is classified with minimum hardware complexity and maximum speed using straight lines, circles, parabolas etc. RK algorithm-based devices (RKD) and mathematical functional blocks classify the feature space very rapidly after learning pattern classification with a fewer numbers of training sets compared to other statistical and artificial neural network (ANN) methods. RKDs are self-learning and fast responding devices and which manipulate a single variable at a time. The RK algorithm is used for learning the range of a variable. A set of sample variable and their corresponding responds are given for learning. The mathematical functional blocks manipulate one or more variables or attributes to perform mathematical functions and the outputs of these blocks are fed to RKDs. Finally, the RKDs perform the classification functions. The classification using straight lines or curves depends upon the mathematical functional block.