44,628 research outputs found
A Framework of Fuzzy Diagnosis
Fault diagnosis has become an important component in intelligent systems, such as intelligent control systems and intelligent eLearning systems. Reiter's diagnosis theory, described by first-order sentences, has been attracting much attention in this field. However, descriptions and observations of most real-world situations are related to fuzziness because of the incompleteness and the uncertainty of knowledge, e. g., the fault diagnosis of student behaviors in the eLearning processes. In this paper, an extension of Reiter's consistency-based diagnosis methodology, Fuzzy Diagnosis, has been proposed, which is able to deal with incomplete or fuzzy knowledge. A number of important properties of the Fuzzy diagnoses schemes have also been established. The computing of fuzzy diagnoses is mapped to solving a system of inequalities. Some special cases, abstracted from real-world situations, have been discussed. In particular, the fuzzy diagnosis problem, in which fuzzy observations are represented by clause-style fuzzy theories, has been presented and its solving method has also been given. A student fault diagnostic problem abstracted from a simplified real-world eLearning case is described to demonstrate the application of our diagnostic framework
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems,
for short) is a variant of Spiking Neural P systems incorporating
fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge
and reasoning required for fault diagnosis applications. In this sense,
several FRSN P system variants have been proposed, dealing with real
numbers, trapezoidal numbers, weights, etc. The model incorporating
real numbers was the first introduced [13], presenting promising applications
in the field of fault diagnosis of electrical systems. For this variant,
a matrix-based algorithm was provided which, when executed on parallel
computing platforms, fully exploits the model maximally parallel
capacities. In this paper we introduce a P-Lingua framework extension
to parse and simulate FRSN P systems with real numbers. Two simulators,
implementing a variant of the original matrix-based simulation
algorithm, are provided: a sequential one (written in Java), intended to
run on traditional CPUs, and a parallel one, intended to run on CUDAenabled
devices.Ministerio de Economía y Competitividad TIN2012-3743
Intertemporal Choice of Fuzzy Soft Sets
This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie
Diagnosability of Fuzzy Discrete Event Systems
In order to more effectively cope with the real-world problems of vagueness,
{\it fuzzy discrete event systems} (FDESs) were proposed recently, and the
supervisory control theory of FDESs was developed. In view of the importance of
failure diagnosis, in this paper, we present an approach of the failure
diagnosis in the framework of FDESs. More specifically: (1) We formalize the
definition of diagnosability for FDESs, in which the observable set and failure
set of events are {\it fuzzy}, that is, each event has certain degree to be
observable and unobservable, and, also, each event may possess different
possibility of failure occurring. (2) Through the construction of
observability-based diagnosers of FDESs, we investigate its some basic
properties. In particular, we present a necessary and sufficient condition for
diagnosability of FDESs. (3) Some examples serving to illuminate the
applications of the diagnosability of FDESs are described. To conclude, some
related issues are raised for further consideration.Comment: 14 pages; revisions have been mad
An integrated framework for clinical problem solving in agriculture.
The goal of this paper is to give an overview of a general problem-solving framework for diagnosis, investigation and treatment tasks, that incorporates concepts of abductive inference, fuzzy set logic and decision theory. In this work we focus on the use of this framework in agriculture, with an illustration in corn plantations. The general framework models time durations and intensity of manifestations as fuzzy sets, and, in the particular case of agriculture, it takes into account the favorable conditions for the development of a given disorder and the severity of its manifestations to recommend a treatment, together with other important factors such as risk and cost
An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis
Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
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