engleski

Application of Fuzzy Logic in Diet Therapy - Advantages of Application

Computing, in its usual sense, is centred on manipulation of numbers and symbols. In contrasts, computing with linguistic variables is a methodology in which the object of computation are words and propositions drawn from a natural language, e.g., significant increase in price, small, large, far from recommendations, etc. Computing with words is inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computation. A basic difference between perception and measurements is that, in general, measurements are crisp whereas perceptions are fuzzy. Instead of Boolean logic, fuzzy logic uses a collection of fuzzy variables defined by membership functions and inference rules. Most of traditional tools for formal modelling, reasoning, and computing are crisp, deterministic, and precise in character. This methodology is a part of mathematical theories of artificial intelligence. Human nutrition, notably the diet evaluation, considering the daily intake of energy and nutrients, is often explained by computing with words, for instance the final conclusion regarding an analysed diet plan can result with phrases as: “the intake of Na should be considerably reduced” or “the consumption of fruits and vegetables must be increased”. The theory of fuzzy logic was used in the planning and management of expenses in social nourishment concerning also the nutritive structure of meals. Modelling and planning of nourishment includes a number of unspecified characteristics, which are depended on nutrient offer and also on age, gender and profession of a person (concerning the physical activity level) or population group. Some recommended nutrient and energy intakes are given as single numbers (crisp values). But for most nutrients are also given the average requirements (AR), the lowest threshold intake (LTI) and the calculated population reference intake (PRI). These intervals and the values of LTI, AR and PRI do not represent the full reality, which is a continuous transition from critical low intake to adequate intake to excess or even toxic amounts. In this work the daily recommendations as crisp numbers are modelled as fuzzy sets. The daily recommended intake (DRI) for each observed nutrient and energy intake is “softened” by introduction of membership function of fuzzy sets defined for each individual nutrient

fuzzy logic ; nutrition ; modeling

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