PRESETATION BY: TEJAS KAVITAKE DANIEL AKU YIRANG

INTRODUCTION Color recipe prediction is a practical application of soft computing. Color recipe prediction introduces “Neuro-fuzzy methodology” and “Computational intelligence” It combines 3 principles of soft computing components: (i) Fuzzy systems (ii) Neural networks (iii) Genetic algorithms

i/p & o/p relation in color recipe prediction Target color 16 inputs Surface spectral reflectance White Black Green 1 Red 1 Green 2 Violet Red 2 Yellow 1 Yellow 2 Blue 10 outputs Color recipe prediction system

Main concerns in color recipe prediction It is difficult to predict precise colorant concentrations{ we need to specify levels such as 0.01%}. It is necessary to specify use of limited number of colorants and we need to avoid the use of complementary colorants. The magnitude of mean-squared error of colorant vectors may not correspond exactly to that of color differences. It is important to consider human visual sensitivity to color difference, which may be costly. Some different combinations of colorant may have the same perceptual attributes of color as seen by humans.

Canfis modeling CANFIS basically stands for “ CoActive Neuro-fuzzy Inference Systems “. In this section we show how neuro fuzzy models can be generalized for application to color recipe prediction.

Understanding basic terms MF’s : Membership Functions Hue : A perceptual attribute of color which is linguistic variable W : Firing strength {eg : for firing strength of yellow color we write W(y)}

CANFIS Architectures Understanding with an example : YELLOW RULE : if the target color is “yellow”, then use yellow rule, C(y). YELLOW RULE 1 : if the target color is “greenish yellow” then use a “greenish yellow” rule, C(gy). YELLOW RULE 2 : if the target color is “ very yellow” then use a “very yellow” rule, C(vy). YELLOW RULE 3 : if the target color is “reddish yellow” then use a “reddish yellow” rule, C(ry).

Canfis with five color rules for color recipe prediction

Thank you……….