The Use of Fundamental Color Stimulus to Improve the Performance of Artificial Neural Network Color Match Prediction Systems

Abstract

In the present investigation attempts were made for the first time to use the fundamental color stimulus as the input for a fixed optimized neural network match prediction system. Four sets of data having different origins (i.e. different substrate, different colorant sets and different dyeing procedures) were used to train and test the performance of the network. The results showed that the use of fundamental color stimulus greatly reduces the errors as depicted by the MSE and ∆ Cave data and improves the performance of the neural network prediction system. Additionally the use of fundamental color stimulus makes provisions for predicting the concentrations of one data set whilst being trained by a second data set of completely different origin.