Supervision des régulateurs par logique floue

Classical control theory is based upon an analytic system design procedure using deterministic or stochastic approaches. In the past years, there has been a growing interest in using new computational and machine intelligence technologies such as rule-based systems, fuzzy logic or neural networks in process control. These approaches are often referred to as expert or qualitative control. Fuzzy logic, in particular, has been successfully applied to various industrial control problems. Generally speaking, fuzzy logic and rule-based techniques are a means of dealing with imprecision, a method of modeling human behavior, allowing to incorporate heuristic knowledge and symbolic information into process calculators. This approach becomes particularly interesting when a sufficient analytic representation of the process is too difficult or even impossible to obtain. However, although expert control is more and more accepted in the control engineering community, its place among conventional techniques has not been defined clearly. This is primarily due to the lack of a general methodology to systematically validate the global functioning and performance of systems that incorporate intelligent components. Nevertheless, it can be observed that expert control and symbolic data processing have the potential to improve the performance of a feedback loop and to provide new functions in a control system. As a part of a research program to study the potential role of fuzzy logic for future aircraft control systems, this paper focuses on the conception of supervisory control structures for aeronautical applications using rule based expert systems and fuzzy logic theory. This concept can be used to backup or monitor a conventional control system improving its performance in the presence of a changing system environment or in extreme operating conditions. The effectiveness of this method could be successfully demonstrated by two application examples, which will be discussed in this paper

Interface tactile pour la saisie guidée de connaissances

International audienceIn recent years, artificial intelligence tools have democratized and are increasingly used by people who are not experts in the field. These artificial intelligence tools, like rule-based or constraint-based systems require the input of human expertise to replicate the desired reasoning. Despite the explosion of new devices and new input paradigms, such as tablets and other touch interfaces, it seems that the usability of these tools have not taken advantage of these recent advances. In this article, we illustrate our concept with the rule edition in a fuzzy expert system. The special feature of fuzzy logic is that these rules look closer to natural language than classical logic. We present our work that involves the use of new touch interfaces to edit a fuzzy rule base with one finger. We end this section by the evaluation of the interface with a user panel.Au cours de ces dernières années, les outils d'intelligence artificielle se sont démocratisés et sont de plus en plus sou-vent utilisés par des personnes qui ne sont pas expertes du domaine. Parmi ces outils d'intelligence artificielle, les systèmes à base de règles ou de contraintes nécessitent la saisie de l'expertise humaine afin de reproduire le comporte-ment souhaité. Malgré l'explosion des nouveaux périphé-riques et de nouveaux paradigmes de saisie, comme les tablettes et autres interfaces tactiles, l'ergonomie de ces outils semble ne pas avoir profité de toutes ces avancées récentes. Dans cet article, nous prenons l'exemple d'un système expert flou pour lequel il faut rédiger des règles. La particu-larité de la logique floue est que ces règles sont construites d'une manière plus proche du langage naturel qu'en lo-gique classique. Nous présentons notre travail qui consiste en l'exploitation des nouvelles interfaces tactiles afin de rédiger une base de règles floues avec un seul doigt. Nous terminons cet article par l'évaluation de l'interface auprès d'un panel d'utilisateurs

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Characterizing urban landscapes using fuzzy sets

Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of “urban” on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further, certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns, transportation, building type and density while ecological components include soils, elevation, hydrology, climate, vegetation and tree cover. In this paper, we adopt a coupled human and natural system (CHANS) integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of “urban characterization” since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications, user-defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from “highly urban” to “least urban”. A socio-economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership, in each case, and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability, urban ecology and urban metabolism.This research was partially supported by "Boston University Initiative on Cities Early Stage Urban Research Awards 2015-16" (Gopal & Phillips) and the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. (Boston University Initiative on Cities Early Stage Urban Research Awards; Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University)https://doi.org/10.1016/j.compenvurbsys.2016.02.002Published versio

Expert supervision of an anti-skid control system of a commercial aircraft

A rule-based supervising system that incorporates fuzzy logic has been designed to back-up a conventional anti-skid braking system (ABS). Expressing the expert knowledge about the ABS in terms of linguistic rules, the supervising fuzzy system adapts the reference wheel slip of the ABS with respect to the actual runway condition. Two approaches are presented: The first uses a simple rule-based decision logic, which evaluates a new reference slip directly from the measured system variables. The second approach employes an explicit identification of the runway condition, which is used as input information of a fuzzy system to evaluate a new reference slip. This application example demonstrates the capabilities of a parallel use of conventional control techniques and fuzzy logic

Expert supervision of conventional control systems

The objective of this paper is to outline a general concept for the design of supervising fuzzy controllers to back up or monitor a conventzonal control system. The use of fuzzy logic in an external, hierarchacal control structure provides a systematic approach to integrate heuristics in a conventional control loop. Supervising techniques become especially interesting, when the system to be controlled is highly non-linear (parameter variation, saturation of the control surfaces etc.). By the means of two application examples it will be shown, how this method can effectively be used to improve the performance of a conventional control system. Both examples are part of an extended research project that is being carried out at Akrospatiale and E.N.S.I.C.A. in France to study the role of fuzzy control for potential applications in aircraft control systems