Genetic Optimisation of Neural Network Architectures for Colour Recipe Prediction

Abstract

Colour control systems based on spectrophotometers and microprocessors are finding increased use in production environments. One of the most important aspects of quality control in manufacturing processes is the maintenance of colour in the product. This involves selecting a recipe of appropriate dyes or pigments which when applied at a specific concentration to the product will render the required colour. This process is known as recipe prediction and is traditionally carried out by trained colourists who achieve a colour match via a combination of experience and trial-and-error. Instrumental recipe prediction was introduced commercially in the 1960’s and has become one of the most important industrial applications of colorimetry. The model that is almost exclusively used is known as the Kubelka-Munk theory, however its operation in certain areas of coloration is such as to warrant an alternative approach. The purpose of this paper is to investigate the performance of a Genetically optimised Neural Network applied to this recipe prediction task.