Fuzzy Inductive Reasoning for Variable Selection Analysis and Modelling of Biological Systems

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Abstract

Fuzzy inductive reasoning (FIR) is a qualitative inductive modelling and simulation methodology for dealing with dynamical systems. It has proven to be a powerful tool for qualitative model identification and prediction of future behaviour of various kinds of dynamical systems, especially from the soft sciences, such as biology, biomedicine and ecology. This paper focuses on modelling aspects of the FIR methodology. It is shown that the FIR variable selection analysis is a useful tool not only for FIR but also for other classical quantitative methodologies such as nonlinear autoregressive moving average modelling with external inputs (NARMAX). The tool allows us to obtain models that interpret a system under study in optimal ways, in the sense that these models are well suited for predicting the future behaviour of the system they represent. The FIR variable selection analysis turns out to work well even in those applications where standard statistical variable selection analysis does not provide any useful information. In this paper, the FIR variable selection analysis is applied to a real system stemming from biology, namely, shrimp farming. The main goal is the identification of a growth model for occidental white shrimp (Penaeus vannamei), which allows farmers to plan the dates for seeding and harvesting the ponds in order to maximise their profits. FIR and NARMAX shrimp growth models are identified, and their prediction capabilities are compared to each other.

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Last modified: October 5, 2009 -- © François Cellier