Fuzzy logic controllers: A knowledge-based system perspective

Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems

A fuzzy random forest

AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a fuzzy random forest, is proposed. This approach combines the robustness of multiple classifier systems, the power of the randomness to increase the diversity of the trees, and the flexibility of fuzzy logic and fuzzy sets for imperfect data management. Various combination methods to obtain the final decision of the multiple classifier system are proposed and compared. Some of them are weighted combination methods which make a weighting of the decisions of the different elements of the multiple classifier system (leaves or trees). A comparative study with several datasets is made to show the efficiency of the proposed multiple classifier system and the various combination methods. The proposed multiple classifier system exhibits a good accuracy classification, comparable to that of the best classifiers when tested with conventional data sets. However, unlike other classifiers, the proposed classifier provides a similar accuracy when tested with imperfect datasets (with missing and fuzzy values) and with datasets with noise

Tratamiento terciario de un efluente de citrícola por ficorremediación

Argentina es el principal productor mundial de limón, siendo la provincia de Tucumán el mayor productor del país. El proceso productivo citrícola genera miles de litros de efluentes que deben ser tratados antes ser vertidos al medio ambiente. La ficorremediación es una estrategia de biorremediación de contaminantes que hace uso de algas y microalgas. Existen estudios de biorremediación de efluentes con microalgas, pero ninguno es aplicado como tratamiento terciario para un ajuste en los parámetros del agua vertida por las citrícolas. La biomasa microalgal tiene diversas aplicaciones, tanto en el campo de obtención de proteínas, o generación de biocombustibles de 4ta generación. Por lo tanto, el objetivo de este proyecto de investigación es la depuración del efluente de industria citrícola por ficorremediación. El proyecto se llevó a cabo adaptando la cepa Scenedesmus acutus a un efluente de citrícola de la provincia de Tucumán por diluciones con un medio de sales inorgánicas (Bold´s Basal Medium). Las experiencias se realizaron a escala laboratorio con un fotoperiodo de 12hs y se proveyó de aireación. Se procedió a realizar el tratamiento del efluente y a la producción de biomasa en biorreactores de 5L tipo “batch", adoptando las mismas condiciones de cultivo. Al comienzo y al final del tratamiento de efluentes, se tomaron muestras para el estudio de los parámetros del agua. Después del tratamiento de efluente citrícola con microalgas se produjo una remoción del 96% de DBO, 86% de DQO y un 81% de eliminación de Nitrógeno, con mejoras en los valores permitidos para ser utilizado en riegoFil: Varela Bonissone del Valle, Emma . Universidad Nacional de TucumánFil: Vicente, Paula Florencia. Universidad Nacional de TucumánFil: Herrera, Rodrigo Exequiel. Universidad Nacional de Tucumá

On the semantics of fuzzy logic

AbstractThis paper presents a formal characterization of the major concepts and constructs of fuzzy logic in terms of notions of distance, closeness, and similarity between pairs of possible worlds. The formalism is a direct extension (by recognition of multiple degrees of accessibility, conceivability, or reachability) of the najor modal logic concepts of possible and necessary truth.Given a function that maps pairs of possible worlds into a number between 0 and 1, generalizing the conventional concept of an equivalence relation, the major constructs of fuzzy logic (conditional and unconditioned possibility distributions) are defined in terms of this similarity relation using familiar concepts from the mathematical theory of metric spaces. This interpretation is different in nature and character from the typical, chance-oriented, meanings associated with probabilistic concepts, which are grounded on the mathematical notion of set measure. The similarity structure defines a topological notion of continuity in the space of possible worlds (and in that of its subsets, i.e., propositions) that allows a form of logical “extrapolation” between possible worlds.This logical extrapolation operation corresponds to the major deductive rule of fuzzy logic — the compositional rule of inference or generalized modus ponens of Zadeh — an inferential operation that generalizes its classical counterpart by virtue of its ability to be utilized when propositions representing available evidence match only approximately the antecedents of conditional propositions. The relations between the similarity-based interpretation of the role of conditional possibility distributions and the approximate inferential procedures of Baldwin are also discussed.A straightforward extension of the theory to the case where the similarity scale is symbolic rather than numeric is described. The problem of generating similarity functions from a given set of possibility distributions, with the latter interpreted as defining a number of (graded) discernibility relations and the former as the result of combining them into a joint measure of distinguishability between possible worlds, is briefly discussed

Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself. © 2004 Elsevier B.V. All rights reserved