Author
Mohammad Reza Rajati
Other affiliations: Amirkabir University of Technology, Chevron Corporation, K.N.Toosi University of Technology
Bio: Mohammad Reza Rajati is an academic researcher from University of Southern California. The author has contributed to research in topics: Fuzzy set & Type-2 fuzzy sets and systems. The author has an hindex of 11, co-authored 37 publications receiving 333 citations. Previous affiliations of Mohammad Reza Rajati include Amirkabir University of Technology & Chevron Corporation.
Papers
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TL;DR: The evolution of how the primary membership has been used in both the mathematical descriptions of a T2 FS and its footprint of uncertainty (FOU) is summarized and recommendations notational changes are provided that can be used by all authors.
119 citations
TL;DR: A novel loss function is proposed that gives rise to a novel method, Outlier Exposure with Confidence Control (OECC), which achieves superior results in out-of-distribution detection with OE both on image and text classification tasks without requiring access to OOD samples.
67 citations
TL;DR: It is shown that, when fuzzy set models of words are obtained by collecting data from subjects in a Computing with Words setting, interactive addition of fuzzy sets is not a well-defined method and the optimization problems related to it may have no solutions, although interactive addition is recommended in the literature for solving multicriteria decision making problems and for dealing with uncertain probabilities.
27 citations
TL;DR: The solutions of the ACWW problems, which involve the fuzzy set models of the words, are formulated using the Generalized Extension Principle and decoded into natural language words using Jaccard's similarity measure.
Abstract: In this paper, we propose and demonstrate an effective methodology for implementing the generalized extension principle to solve Advanced Computing with Words (ACWW) problems. Such problems involve implicit assignments of linguistic truth, probability, and possibility. To begin, we establish the vocabularies of the words involved in the problems, and then collect data from subjects about the words after which fuzzy set models for the words are obtained by using the Interval Approach (IA) or the Enhanced Interval Approach (EIA). Next, the solutions of the ACWW problems, which involve the fuzzy set models of the words, are formulated using the Generalized Extension Principle. Because the solutions to those problems involve complicated functional optimization problems that cannot be solved analytically, we then develop a numerical method for their solution. Finally, the resulting fuzzy set solutions are decoded into natural language words using Jaccard's similarity measure. We explain how ACWW problems can solve some potential prototype engineering problems and connect the methodology of this paper with Perceptual Computing.
23 citations
TL;DR: This paper explains how to compute normalized interval type-2 fuzzy sets in closed form and explains how the results reduce to well-known results for type-1 fuzzy sets and interval sets.
Abstract: This paper explains how to compute normalized interval type-2 fuzzy sets in closed form and explains how the results reduce to well-known results for type-1 fuzzy sets and interval sets. Such normalized interval type-2 fuzzy sets may be needed in linguistic probability computa- tions or multiple criteria decision analysis under uncertainty. Index Terms—Fuzzy weighted average (FWA), interval type-2 fuzzy sets (IT2 FSs), interval weighted average (IWA), linguistic probability, linguistic weighted average (LWA), normalized interval type-2 fuzzy sets.
22 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
Journal Article•
01 Jan 1999-Centre for Health Research; Faculty of Health; Institute of Health and Biomedical Innovation
TL;DR: In this article, the authors examined the combined effects of self-identity and social identity constructs on intention and behaviour, and examined the effect of selfidentity as a function of past experience of performing the behaviour.
Abstract: The aim of the present study was to examine further the role that self-identity plays in the theory of planned behaviour and, more specifically, to: (1) examine the combined effects of self-identity and social identity constructs on intention and behaviour, and (2) examine the effects of self-identity as a function of past experience of performing the behaviour. The study was concerned with the prediction of intention to engage in household recycling and reported recycling behaviour. A sample of 143 community residents participated in the study. It was prospective in design: measures of the predictors and intention were obtained at the first wave of data collection, whereas behaviour was assessed two weeks later. Selfidentity significantly predicted behavioural intention, a relationship that was not dependent on the extent to which the behaviour had been performed in the past. As expected, there was also evidence that the perceived norm of a behaviourally relevant reference group was related to behavioural intention, but only for participants who identified strongly with the group, whereas the relationship between perceived behavioural control (a personal factor) and intention was strongest for low identifiers.
955 citations
TL;DR: This paper explains two fundamental differences between IT2 and T1 FLCs: Adaptiveness and Novelty, meaning that the upper and lower membership functions of the same IT2 fuzzy set may be used simultaneously in computing each bound of the type-reduced interval.
Abstract: Interval type-2 fuzzy logic controllers (IT2 FLCs) have recently been attracting a lot of research attention. Many reported results have shown that IT2 FLCs are better able to handle uncertainties than their type-1 (T1) counterparts. A challenging question is the following: What are the fundamental differences between IT2 and T1 FLCs? Once the fundamental differences are clear, we can better understand the advantages of IT2 FLCs and, hence, make better use of them. This paper explains two fundamental differences between IT2 and T1 FLCs: 1) Adaptiveness, meaning that the embedded T1 fuzzy sets used to compute the bounds of the type-reduced interval change as input changes; and 2) Novelty, meaning that the upper and lower membership functions of the same IT2 fuzzy set may be used simultaneously in computing each bound of the type-reduced interval. T1 FLCs do not have these properties; thus, a T1 FLC cannot implement the complex control surface of an IT2 FLC given the same rulebase. We also present several methods to visualize and analyze the effects of these two fundamental differences, including the control surface, the P-map, the equivalent generalized T1 fuzzy sets, and the equivalent PI gains. Finally, we examine five alternative type reducers for IT2 FLCs and explain why they do not capture the fundamentals of IT2 FLCs.
253 citations
TL;DR: In this paper, a novel intelligent method is applied to the problem of sizing in a hybrid power system such that the demand of residential area is met, where the system consists of fuel cells, some wind units, some electrolyzers, a reformer, an anaerobic reactor and some hydrogen tanks.
238 citations
TL;DR: An integrated methodology to address MCGDM problems based on the best-worst method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique in an interval type-2 fuzzy environment is provided.
214 citations