Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/24692
Title: Highest standard count estimation from fibre parameters using neural network techniques
Authors: Shanmugam, N
Doke, S S
Keywords: Artificial neural network;Back propagation neural network;Cotton;Fibre quality index;Highest standard count;Lea CSP;Multiple regression model
Issue Date: Sep-2005
Publisher: NISCAIR-CSIR, India
IPC Code: Int. CI.7 D06H3/00, G06N3/02
Abstract: Artificial neural network (ANN) model has been developed for predicting highest standard count (HSC) from fibre properties, namely 2.5% span length, uniformity ratio, micronaire and bundle strength. The developed ANN model was compared with the multiple regression and fibre quality index (FQI) based regression models. ANN ranking of fibre properties was carried out using difference in test performance values as indicator and in case of multiple regression, standardized regression coefficients were used. It has been observed that in both ANN and multiple regression models, the ranks of span length and bundle strength are the same. The span length is the largest contributor for HSC and the bundle strength is the least contributor. The mean absolute errors of ANN and multiple regression equation are found to be less by 15 % and 11% respectively in comparison with FQI-based linear regression equation.
Page(s): 302-308
URI: http://hdl.handle.net/123456789/24692
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.30(3) [September 2005]

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