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Weakly implicative (fuzzy) logics
 Institute of Computer
, 2004
"... In Abstract Algebraic Logic (see e.g. [2] or [3]) a classification of wellbehaved logical systems, called ‘Leibniz hierarchy’, is based on the properties of Leibniz operator mapping every filter on an algebra to the maximum logical congruence compatible with the filter (i.e. not identifying element ..."
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Cited by 4 (0 self)
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In Abstract Algebraic Logic (see e.g. [2] or [3]) a classification of wellbehaved logical systems, called ‘Leibniz hierarchy’, is based on the properties of Leibniz operator mapping every filter on an algebra to the maximum logical congruence compatible with the filter (i.e. not identifying
Rulebase structure identification in an adaptivenetworkbased fuzzy inference system
 IEEE Trans. Fuzzy Syst
, 1994
"... AbstructFuzzy rulebase modeling is the task of identifying the structure and the parameters of a fuzzy IFTHEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to finetune membership functions in a fuzzy rule base has received more and more attention ..."
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Cited by 33 (0 self)
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properties: modeling accuracy and pattern matching efficiency. Based on the analysis, we suggest a bottomup approach of using rule organization to meet the conflicting requirements. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against
© Hindawi Publishing Corp. ON IMAGINABLE TFUZZY SUBALGEBRAS AND IMAGINABLE TFUZZY CLOSED IDEALS IN BCHALGEBRAS
, 2000
"... Abstract. We inquire further into the properties on fuzzy closed ideals. We give a characterization of a fuzzy closed ideal using its level set, and establish some conditions for a fuzzy set to be a fuzzy closed ideal. We describe the fuzzy closed ideal generated by a fuzzy set, and give a characte ..."
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Abstract. We inquire further into the properties on fuzzy closed ideals. We give a characterization of a fuzzy closed ideal using its level set, and establish some conditions for a fuzzy set to be a fuzzy closed ideal. We describe the fuzzy closed ideal generated by a fuzzy set, and give a
Inverse problems: fuzzy representation of uncertainty generates a regularization
 NASA Johnson Space Center
, 1992
"... Abstract. In many applied problems (geophysics, medicine, astronomy, etc) we cannot directly measure the values x(t) of the desired physical quantity x in different moments of time, so we measure some related quantity y(t), and then we try to reconstruct the desired values x(t). This problem is ofte ..."
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Cited by 11 (11 self)
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) from the set of all possible solutions are called regularization methods. In some cases, we know the statistical characteristics both of x(t) and of the measurement errors, so we can apply statistical filtering methods (welldeveloped since the invention of a Wiener filter). In some situations, we know
ELSEVIER Fuzzy Sets and Systems 77 (1996) 1533 Fuzzy prediction and filtering in impulsive noise
"... Additive fuzzy systems can filter impulsive noise from signals. Alphastable statistics model the impulsiveness as a parametrized family of probability density functions or unitarea bell curves. The bellcurve parameter ct ranges through the interval (0, 2] and gives the Gaussian bell curve when 0t ..."
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Additive fuzzy systems can filter impulsive noise from signals. Alphastable statistics model the impulsiveness as a parametrized family of probability density functions or unitarea bell curves. The bellcurve parameter ct ranges through the interval (0, 2] and gives the Gaussian bell curve when 0
DETC200849563 TOPOLOGY OPTIMIZATION OF A MEMS RESONATOR USING HYBRID FUZZY TECHNIQUES
, 2008
"... ABSTRACT This paper introduces a new methodology for the design of structures by geometry and topology optimization accounting for loading and boundary conditions as well as material properties. The Fuzzy Heuristic Gradient Projection (FHGP) method is used as a direct search technique for the geome ..."
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ABSTRACT This paper introduces a new methodology for the design of structures by geometry and topology optimization accounting for loading and boundary conditions as well as material properties. The Fuzzy Heuristic Gradient Projection (FHGP) method is used as a direct search technique
Chapter 12 Rough Sets and Rough Logic: A KDD Perspective
"... Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations of con ..."
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of concepts. This main goal is motivated by the basic fact, constituting also the main problem of KDD, that languages we may choose for knowledge description are incomplete. A fortiori, we have to describe concepts of interest (features, properties, relations etc.) not completely but by means
RICE UNIVERSITY Regime Change: Sampling Rate vs. BitDepth in Compressive Sensing
, 2011
"... The compressive sensing (CS) framework aims to ease the burden on analogtodigital converters (ADCs) by exploiting inherent structure in natural and manmade signals. It has been demonstrated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
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The compressive sensing (CS) framework aims to ease the burden on analogtodigital converters (ADCs) by exploiting inherent structure in natural and manmade signals. It has been demonstrated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sampling rates is quantization, i.e., higher bitdepths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bitdepths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1bit CS framework leads us to scenarios where it may be more appropriate to reduce bitdepth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signaltonoise ratio (SNR). In the measurement