Crisp sets as classes of discontinuous fuzzy sets

AbstractThis paper aims to show how, by using a threshold-based approach, a path from imprecise information to a crisp ‘decision’ can be developed. It deals with the problem of the logical transformation of a fuzzy set into a crisp set. Such threshold arises from the ideas of contradiction and separation, and allows us to prove that crisp sets can be structurally considered as classes of discontinuous fuzzy sets. It is also shown that continuous fuzzy sets are computationally indistinguishable from some kind of discontinuous fuzzy sets

Construction of aggregation operators with noble reinforcement

This paper examines disjunctive aggregation operators used in various recommender systems. A specific requirement in these systems is the property of noble reinforcement: allowing a collection of high-valued arguments to reinforce each other while avoiding reinforcement of low-valued arguments. We present a new construction of Lipschitz-continuous aggregation operators with noble reinforcement property and its refinements. <br /

Approximating Pareto frontier using a hybrid line search approach

This is the post-print version of the final paper published in Information Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.CNCSIS IDEI 2412, Romani

The Environmental Impact Assessment of Cal Poly\u27s Manufacturing Laboratory

The purpose of the study is to obtain basic understanding of the sustainability of Cal Poly IME 144 laboratory. Since the general condition of the lab prevents precise, efficient data collection, fuzzy logic is used to provide an overview of the laboratory\u27s sustainability. Aluminum chips, brass chips, and paper waste are identified as primary waste streams. Electric usage of the laboratory is approximately calculated based on the machines\u27 running time. Fuzzy logic system with three inputs and one output is designed in the Matlab software environment with Simulink to simulate a model for the laboratory\u27s sustainability. Also the study suggests a few alternatives to improve the sustainability based on the model

Robust rank correlation coefficients on the basis of fuzzy

The goal of this paper is to demonstrate that established rank correlation measures are not ideally suited for measuring rank correlation for numerical data that are perturbed by noise. We propose to use robust rank correlation measures based on fuzzy orderings. We demonstrate that the new measures overcome the robustness problems of existing rank correlation coe cients. As a rst step, this is accomplished by illustrative examples. The paper closes with an outlook on future research and applicationsPeer Reviewe

Fuzzy C-Means Algorithm to Diagnose Breast Cancer

The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the Physician in making sense out of the confusion of data. This Paper carried out to generate and evaluate fuzzy model to predict malignancy of breast tumor, using Wisconsin Diagnosis Breast Cancer Database (WDBC). Our objectives in this Paper are: (i) to find the diagnostic performance of fuzzy model in distinction between malignance and benign patterns, (ii) to reduce the number of benign cases sent for biopsy using this model as a supportive tool, and (iii) to validate the capability of this model to recognize new cases