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In this paper, we present a set of tools for the simulation of fuzzy systems. The described methods allow to take into account and to handle a lot of imperfect parameters for the studied systems. The methods developed are based on fuzzy logic and DEVS formalism. Their goal is to expand fields of application of simulation environments, and to foster interdisciplinary collaborations. At first, we have applied them to study the spread of forest fires. This application was developed in collaboration with the fire-fighters.

This paper describes a new methodology to enable large scale high resolution environmental simulation. Unlike the vast majority of environmental modeling techniques that split the space into cells, the use of a vector space is proposed here. A phenomena will then be described by its shape, decomposed in several points that can move using a displacement vector. The shape also have a dynamic structure, as each point can instantiate new point because of a change in the space properties or to obtain a better resolution model. Such vector models are generating less overhead because the phenomena is recomputed only if a part of it is entering into a different space entity with different attributes, using cellular space the model would have been recomputed for each neighboring identical cells. This technique uses the DSDEVS formalism to describe discrete event models with dynamic structure, and will be implemented in the JDEVS toolkit also presented.

For several years, we worked to improve a discrete events modeling formalism: called DEVS. Having defined a method to take into account the inaccuracies iDEVS, in this paper, we present the second part of our research work.Generally, our approach is to associate the DEVS formalism with an object class, which allows using it to new fields of study, and in our case fuzzy systems.This paper describes a new modeling methodology. It allows to modeling and to use fuzzy inference systems (FIS) with DEVS formalism in order to perform the control or the learning on systems described incompletely or with linguistic data. The advantages of this method are numerous: to extend the DEVS formalism to other application fields; to propose new DEVS models for fuzzy inference; to provide users with simple and intuitive modeling methods. Throughout this paper we describe the tools and methods which were developed to make possible the combination of these two approaches.