Abstract-In the present paper we propose a Growing Type-2 Fuzzy Classifier (GT2FC) for online rule learning from real-time data streams. While in batch rule learning the training data are assumed to be drawn from a stationary distribution, in online rule learning data can dynamically change over time becoming potentially non-stationary. To accommodate dynamic change, GT2FC relies on a new semi-supervised online learning algorithm called 2G2M (Growing Gaussian Mixture Model). In particular, 2G2M is used to generate the type-2 fuzzy membership functions to build the type-2 fuzzy rules. GT2FC is designed to accommodate data online and to reconcile labeled and unlabeled data using selflearning. Moreover. GT2FC maintains low complexity of the rule base using online optimization and feature selection mechanisms.GT2FC is tested on data obtained from an ambient intelligence (AmI) application where the goal is to exploit sensed data to monitor the living space on behalf of the inhabitants. Because sensors are prone to faults and noise, type-2 fuzzy modeling is very suitable for dealing with such an application. Thus, GT2FC offers the advantage of dealing with uncertainty in addition to self-adaptation in an online manner. For illustration purposes, GT2FC is also validated on synthetic and classic UCI data sets. The detailed empirical study shows that GT2FC performs very well under various experimental settings.

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This paper describes the GEMS software developed as part of the IRT Saint Exupéry MDA-MDO project 1 for supporting Multidisciplinary Design Optimization (MDO) capabilities. GEMS is a Python library for programing MDO simulation processes, built on top of NumPy 2 , SciPy 3 and Matplotlib 4. GEMS aims at pushing forward the limits of automation in simulation process development, with a particular focus on : i) the automatic programming of MDO processes; ii) distributed and multi-level MDO formulations (or MDO architectures); iii) the integration and development of state-of-the-art algorithms for optimization, design of experiments, surrogate models and coupled analyses; iv) the automation of MDO result analysis; v) the deployment of MDO processes in heterogeneous and distributed industrial simulation environments.

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Robust optimization (RO) has attracted much attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which case the RO problem may be reformulated via worst-case analysis, or as families of distributions. The challenge of RO is to reformulate or approximate robust constraints so that the uncertain optimization problem is transformed into a tractable deterministic optimization problem. Most reformulation methods assume linearity of the robust constraints or uncertainty sets of favorable shape, which represents only a fraction of real-world applications. This survey addresses nonlinear RO and includes problem formulations and applications, solution approaches, and available software with code samples.