Search results for " Set"
showing 10 items of 2095 documents
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Automatic mass spectra recognition for Ultra High Vacuum systems using multilabel classification
2021
Abstract In Ultra High-Vacuum (UHV) systems it is common to find a mixture of many gases originating from surface outgassing, leaks and permeation that contaminate vacuum chambers and cause issues to reach ultimate pressures. The identification of these contaminants is, in general, done manually by trained technicians from the analysis of mass spectra. This task is time consuming and can lead to misinterpretation or partial understanding of issues. The challenge resides in the rapid identification of these contaminants by using some automatic gas identification technique. This paper explores the automatic and simultaneous identification of 80 molecules, including some of the most commonly p…
Introducing a Fuzzy-Pattern Operator in Fuzzy Time Series
2017
In this paper we introduce a fuzzy pattern operator and propose a new weighting fuzzy time series strategy for generating accurate ex-post forecasts. A decision support system is built for managing the weights of the information provided by the historical data, under a fuzzy time series framework. Our procedure analyzes the historical performance of the time series using different experiments, and it classifies the characteristics of the series through a fuzzy operator, providing a trapezoidal fuzzy number as one-step ahead forecast. We also present some numerical results related to the predictive performance of our procedure with time series of financial data sets.
A naïve way of looking at fuzzy sets
2016
In this study, we consider the concept of a predicate (P) in a universe of discourse X from a specific viewpoint, i.e., the informational viewpoint with respect to its linguistic use. Its meaning and its different types are considered, particularly by considering the predicates that are "measurable" and designate a "collective" (P) in X, which is not always a classical subset of X. We show that the collective P manifests itself in different "states" or fuzzy sets, where knowledge and representation depend on the available information regarding the use of the predicate P in X. We also analyze the linguistic concept of a "collective" where the fuzzy sets are nothing other than informational s…
Forecasting portfolio returns using weighted fuzzy time series methods
2016
We propose using weighted fuzzy time series (FTS) methods to forecast the future performance of returns on portfolios. We model the uncertain parameters of the fuzzy portfolio selection models using a possibilistic interval-valued mean approach, and approximate the uncertain future return on a given portfolio by means of a trapezoidal fuzzy number. Introducing some modifications into the classical models of fuzzy time series, based on weighted operators, enables us to generate trapezoidal numbers as forecasts of the future performance of the portfolio returns. This fuzzy forecast makes it possible to approximate both the expected return and the risk of the investment through the value and a…
Optimal control of discrete-time interval type-2 fuzzy-model-based systems with D-stability constraint and control saturation
2016
This paper investigates the optimal control problem for discrete-time interval type-2 (IT2) fuzzy systems with pole constraints. An IT2 fuzzy controller is characterized by two predefined functions, and the membership functions and the premise rules of the IT2 fuzzy controller can be chosen freely. The pole assignment is considered, which is constrained in a presented disk region. Based on Lyapunov stability theory, sufficient conditions of asymptotic stability with an H ∞ performance are obtained for the discrete-time IT2 fuzzy model based (FMB) system. Based on the criterion, the desired IT2 state-feedback controller is designed to guarantee that the closed-loop system is asymptotically s…
Opinion Dynamics and Stubbornness via Multi-Population Mean-Field Games
2016
This paper studies opinion dynamics for a set of heterogeneous populations of individuals pursuing two conflicting goals: to seek consensus and to be coherent with their initial opinions. The multi-population game under investigation is characterized by (i) rational agents who behave strategically, (ii) heterogeneous populations, and (iii) opinions evolving in response to local interactions. The main contribution of this paper is to encompass all of these aspects under the unified framework of mean-field game theory. We show that, assuming initial Gaussian density functions and affine control policies, the Fokker---Planck---Kolmogorov equation preserves Gaussianity over time. This fact is t…
Decomposition and Mean-Field Approach to Mixed Integer Optimal Compensation Problems
2016
Mixed integer optimal compensation deals with optimization problems with integer- and real-valued control variables to compensate disturbances in dynamic systems. The mixed integer nature of controls could lead to intractability in problems of large dimensions. To address this challenge, we introduce a decomposition method which turns the original n-dimensional optimization problem into n independent scalar problems of lot sizing form. Each of these problems can be viewed as a two-player zero-sum game, which introduces some element of conservatism. Each scalar problem is then reformulated as a shortest path one and solved through linear programming over a receding horizon, a step that mirro…
Designing Cognitive Cities
2018
The following text intends to give an introduction into some of the basic ideas which determined the conception of this book. Thus, the first part of this article introduces the terms “City”, “Smart City” and “Cognitive City”. The second part gives an overview of design theories and approaches such as Action Design Research and Ontological Design (a concept in-the-making), in order to deduce from a theoretical point of view some of the principles that needs to be taken into account when designing the Cognitive City. The third part highlights some concrete techniques that can be usefully applied to the problem of citizen communication for Cognitive Cities (namely Metaheuristics, Fuzzy Sets a…
Wind component estimation for UAS flying in turbulent air
2019
One of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved.