14,532 research outputs found
Fuzzy rule-based system applied to risk estimation of cardiovascular patients
Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc
Approximate Reasoning with Fuzzy Booleans
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp antecedents; in case of fuzzy booleans this set contains only two rules. It is shown that Zadeh's extension principle is equivalent to the compositional rule of inference using a complete set of fuzzy rules with singleton crisp antecedents. The results are applied to describe the use of approximate reasoning with fuzzy booleans to object-oriented design methods
Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm
Fuzzy rule based models have a capability to approximate any continuous
function to any degree of accuracy on a compact domain. The majority of FLC
design process relies on heuristic knowledge of experience operators. In order
to make the design process automatic we present a genetic approach to learn
fuzzy rules as well as membership function parameters. Moreover, several
statistical information criteria such as the Akaike information criterion
(AIC), the Bhansali-Downham information criterion (BDIC), and the
Schwarz-Rissanen information criterion (SRIC) are used to construct optimal
fuzzy models by reducing fuzzy rules. A genetic scheme is used to design
Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule
parameters and the identification of the consequent parameters. Computer
simulations are presented confirming the performance of the constructed fuzzy
logic controller
Comparisons Of Membership Functions For Fuzzy Rules
Different methodologies have been used to design and model a system which having the ability to make decision in uncertain and indecisive situation. In this paper we present the comparisons of membership and reduced membership functions for fuzzy rules. A model of different fuzzy rules is designed for three membership functions (mf) and then reduced the fuzzy rules by reducing the mf.
Using Fuzzy Rules in Identifying Cybercrime in Iranian Banking System
Similar growth of security and information technology and non-slot between these two subjects are factors of comfort in human societies. Therefore, base on evidences, with popularity and prevalence of using internet, cybercrime increases everyday because of failure of achieving balanced growth points, so that methods of attacks and fraud have become more complex. Therefore, security of cyberspace is major concern of banks, corporations and insurance companies. The main goal of this paper is using fuzzy algorithm and recommending effective systemrsquos cybercrime identification. Proposed frameworks, identifies and reports suspected cases in two levels. First level is analysis of user information and second one is detecting wrong warnings
Fuzzy Rules Optimization in Fuzzy Expert System for Machinability Data Selection: Genetic Algorithms Approach
Machinability data selection is complex and cannot be easily formulated by any mathematical model to meet design specification. Fuzzy logic is a good approach to solve
such problems. Fuzzy rules optimization is always a problems for a complex fuzzy rules from more than 10 thousand combinations. (Wong et aL 1997) developed fuzzy models for machinability data selection. There are more than 2 x 1029 possible sets of rules for each model. Situation would be more complicated if further increase the number of inputs and/or outputs. The fuzzy rules were selected by trial and error and intuition in
reference (Wong et aL 1997). Genetic optimization is suggested in this paper to further
optimizing the fuzzy rules optimization with genetic algorithms has been developed.
Weighted centroid method is used for output defuzzi fication to save processing time.
Comparisons between the results of the new models and the previously published
literatures are made
Clustering algorithms for fuzzy rules decomposition
This paper presents the development, testing
and evaluation of generalized Possibilistic
fuzzy c-means (FCM) algorithms applied to
fuzzy sets. Clustering is formulated as a
constrained minimization problem, whose
solution depends on the constraints imposed
on the membership function of the cluster and
on the relevance measure of the fuzzy rules.
This fuzzy clustering of fuzzy rules leads to a
fuzzy partition of the fuzzy rules, one for each
cluster, which corresponds to a new set of
fuzzy sub-systems. When applied to the
clustering of a flat fuzzy system results a set
of decomposed sub-systems that will be
conveniently linked into a Hierarchical
Prioritized Structures
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