https://doi.org/10.15255/KUI.2002.027
Published: Kem. Ind. 52 (5) (2003) 195–200
Paper reference number: KUI-27/2002
Paper type: Original scientific paper

Fuzzy Modelling and Optimising Used in Meal Planning

J. Gajdoš Kljusurić and Ž. Kurtanjek

Abstract

For the construction of a membership function of a fuzzy set, five points are used (Fig. 3): 1. The fuzzy value for zero intake. For essential nutrients this value is 0, for semi-essential nutrients this fuzzy value lies between 0 and 1, and for some other substances (for example alcohol) the optimal status is reached at no intake. The fuzzy value is 1. 2. Safe minimum limit, corresponding to a fuzzy value of 0.9. 3. Optimal intake, corresponding to a fuzzy value of 1. 4. Safe upper limit, corresponding to a fuzzy value of 0.9. 5. The toxic area, corresponding to a fuzzy value of 0. Membership functions are different for different age and gender group. Modelled are membership functions for 11 nutrients (energy, fats, proteins, carbohydrates, minerals: Ca, Fe, vitamins: retinol equivalent, B1, B2, C, niacin). Prerow value, modified harmonic mean (Eq. 4) is used for defuzzification, as it is shown in Fig. 4, as the best method that has a large influence of small values. Using fuzzy logic, present state of meals intended for female and male students aged 14 to 18 years, can be evaluated. Improvements of nutrients share are made by optimisation in the set of fuzzy logic. This is presented in Fig. 6. According to table 1, the results of meal optimisation, using fuzzy logic, show that the meal offer can be in the range of well-balanced supply (table 2-model F2). Modelling of membership functions of a fuzzy set for 11 nutrients are determined according to age and gender following five key points. Prerow value is used for defuzzification of evaluated present state and optimised meal offers. Using fuzzy logic in optimisation and meal planning shows its validity by increasing of PV value for 3-4 times from the nutrient quality of the present state (PS) to the F2 model. Share of other nutrients have also been increased and meat recommendations, what is very important in a stage of intensive growing for teenagers at age of 14 to 18 years.12,15,19 This indicator is more characteristic, it improves meal planning by using fuzzy logic. Important result of optimisation is average daily saving per meal from 7-9 %.

This work is licensed under a Creative Commons Attribution 4.0 International License

Keywords

fuzzy modelling, fuzzy optimisation, membership function, nutrition