Neural Network-Based Active Learning in Multivariate Calibration

Colorimetric Scanner Calibration for Textiles by Neural-Network

In this study, colorimetric calibration of scanner has been done via perceptron neural network with three or four layers by back propagation algorithm for colored polyester fabrics. The results obtained for random training samples are not satisfactory but application of selective training samples for L*a*b* or RGB leads to good results, with better results obtained for the L*a*b* method. On the...

Tutorial Review Neural networks in multivariate calibration

Colorimetric Scanner Calibration for Textiles by Neural-Network

In this study, colorimetric calibration of scanner has been done via perceptron neural network with three or four layers by back propagation algorithm for colored polyester fabrics. The results obtained for random training samples are not satisfactory but application of selective training samples for L*a*b* or RGB leads to good results, with better results obtained for the L*a*b* method. On the...

Two Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate

Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon...

Network-based Learning of Active Noise Control

1 Zbigniew Ogonowski, Silesian University of Technology, 44-100 Gliwice, Akademicka 16, Poland, [email protected] Abstract  The paper presents a concept of network-based learning of Active Noise Control (ANC) problems. Arguments motivating remote experimenting are given. It follows from the presentation that basic experiments can be done automatically using “remote design”. The mo...