median filter based on fuzzy rules and its application to image restoration abstract a novel median type filter controlled by fuzzy rules is proposed in order to remove impulsive noises on signals such as images median filter is well known for removing impulsive noises but this filter distorts the fine structure of signals as well the filter proposed here is obtained as a weighted sum of the input signal and the output of the median filter and the weight is set based on fuzzy rules concerning the states of the input signal sequence moreover this weight is obtained optimally by a learning method so that the mean square error of the filter output for some training signal data can be the minimum some results of image processing show the high performance of this filter moreover the influence of the training signal on the filter performance is shown

partition belief median filter based on dempster shafer theory for image processing a novel median type filter controlled by evidence fusion is proposed for removing noise from images the fusion of evidence based on the dempster shafer evidence theory providing a way to deal with the uncertainty in the evidence fusion indicates to what extent a noise is considered the filter proposed here is obtained as a weighted sum of the current pixel value and the output of the median filter and the weight is set based on the belief value of the input signal sequence the efficient step like function is used to partition the belief space and the least mean square lms algorithm is applied to obtain the optimal weight for each block moreover to improve the performance the new filter is recursively implemented experimental results have demonstrated that the proposed filter can outperform many well accepted median based filters in preserving image details while effectively suppressing impulsive noises and it also works satisfactorily in reducing gaussian as well as the mixture of gaussian and impulsive noise

about simple fuzzy control and fuzzy control based on fuzzy relational equations abstract the paper discusses fuzzy controllers based on fuzzy relational equations in the case when fuzzy controllers inputs are exact godelian implication and minimum function as t norm are considered fuzzy relational equation with sup t composition results in plausible control and adjoint equation results in the simple fuzzy control min max inference method computation of fuzzy relations is not necessary relation between fuzzy plausible control and simple fuzzy control is discussed control algorithms are compared on simulation example

limiting distributions for l sb 1 regression estimators under general conditions it is well known that l 1 estimators of regression parameters are asymptotically normal if the distribution function has a positive derivative at 0 in this paper we derive the asymptotic distributions under more general conditions on the behavior of the distribution function near 0

linear optimization with an arbitrary fuzzy relational inequality in this paper optimization of a linear objective function with fuzzy relational inequality constraints is investigated whereby an arbitrary function is considered as fuzzy composition the feasible solution set is determined and compared with some common results in the literature a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem it is shown that in general a lower bound is always attainable for the optimal objective value moreover we prove that the optimal solution of the problem is obtained if the problem is defined by a non decreasing or non increasing function an algorithm is presented to summarize the process and an example is described to illustrate the algorithm

a least squares model for fuzzy rules of inference abstract in this paper the method of least squares which uses the power function is applied to the fuzzy inference rules these rules in fuzzy logic are known as the generalized modus ponens and the generalized modus tollens the proposed technique gives reasonable evaluations for fuzzy consequences and is simple for computer applications

noise reduction by fuzzy image filtering a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise the filter consists of two stages the first stage computes a fuzzy derivative for eight different directions the second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values both stages are based on fuzzy rules which make use of membership functions the filter can be applied iteratively to effectively reduce heavy noise in particular the shape of the membership functions is adapted according to the remaining noise level after each iteration making use of the distribution of the homogeneity in the image a statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions experimental results are obtained to show the feasibility of the proposed approach these results are also compared to other filters by numerical measures and visual inspection

similarity analysis of inelastic contact analysis of mechanical contact of solids is of interest not only regarding a variety of mechanical assemblies but also on a smaller scale such as roughness properties of surfaces and compaction of powder particles indentation testing is another prominent problem in the context to analyse the phenomena involved is inherently difficult at application essentially due to the presence of large strains nonlinear material behaviour time dependence and moving contact boundaries recently progress has been made however to explicitly solve basic boundary value problems especially due to advances in computational techniques a substantial ingredient which facilitates solution procedures is self similarity and it is the present purpose to explore in detail the advantages in a general setting when this feature prevails a viscoplastic framework is laid down for a wide class of constitutive properties where strain hardening plasticity creep and also nonlinear elasticity arise as special cases it is then shown that when surface shapes and material properties are modelled by homogeneous functions associated boundary value problems posed may be reduced to stationary ones as a consequence within hertzian kinematics relations between contact impression and regions become independent of loading and time and the connection to loading characteristics does not usually require a full solution of the problem in particular it is shown that for general head shapes it proves efficient to use an approach where an intermediate flat die solution serves as a basic tool also for hereditary materials an invariant computational procedure based on the intermediate problem is arrived at and decisive results shown to be found by simple cumulative superposition illustrations are given analytically for ellipsoidal contact of newtonian fluids and by detailed computations for spherical indentation of viscoplastic solids for which also universal hardness formulae are proposed for several bodies in contact it is shown how general results may be extracted from fundamental solutions for a half space

on generalized modus ponens with multiple rules and a residuated implication we consider a multiple rule generalized modus ponens inference scheme with an interpretation based on compositional rule of inference cri and a residuated implication we show that such a system is equivalent as far as cri is concerned to a system that satisfies the basic requirement for fuzzy reasoning proposed by turksen and tian fuzzy sets and systems 58 1993 3 40 we establish an analogous conclusion for an alternative interpretation due to magrez and smets of generalized modus ponens which we call consequent dilation rule cdr this method is computationally faster than cri but it produces less specific inference results

elastic analysis of some punch problems for a layered medium abstract the problems of flat ended cylindrical quadrilateral and triangular punches indenting a layered isotropic elastic half space are considered the former two are analyzed using a basis function technique while the latter problem is analyzed via a singular integral equation solutions are obtained numerically load deflection relations are obtained for a series of values of the ratio of young s modulus in the layer and substrate and for a variety of punch sizes these solutions provide an accurate basis for the estimation of young s modulus of thin films from the initial unloading compliance observed in indentation tests and are specifically relevant to axisymmetric vicker s and triangular indenters the results should also be of interest in foundation engineering

on the development of variable separable asymptotic elastoplastic solutions for interfacial cracks abstract the problem of a plane strain crack lying along the interface between an elasticplastic power law hardening material and a rigid substrate is analysed in detail the possibility of elastoplastic asymptotic crack tip solutions that are separable in r and is explored where r are polar coordinates at the crack tip it is found that such variable separable solutions do exist and the first two terms in the asymptotic expansion of the stress and displacement fields in the neartip region are obtained it is shown that both the first and the second terms in the stress expansion are singular in r as r 0 the asymptotic solution is studied in detail for values of the hardening exponent n near n 1 via a small perturbation of the governing equations about the linear problem it is shown that the leading and second order singular terms in the stress expansion branch from the mode i and mode ii linear elastic solutions respectively the predictions of the asymptotic solution are compared with the results of detailed finite element calculations the region of dominance of the asymptotic solution is found to depend strongly on the geometry of the structure considered and the type of the applied loading

application of partition based median type filters for suppressing noise in images an adaptive median based filter is proposed for removing noise from images specifically the observed sample vector at each pixel location is classified into one of m mutually exclusive partitions each of which has a particular filtering operation the observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of cwm center weighted median filters with variable center weights the estimate at each location is formed as a linear combination of the outputs of those cwm filters and the current pixel value to control the dynamic range of filter outputs a location invariance constraint is imposed upon each weighting vector the weights are optimized using the constrained lms least mean square algorithm recursive implementation of the new filter is then addressed the new technique consistently outperforms other median based filters in suppressing both random valued and fixed valued impulses and it also works satisfactorily in reducing gaussian noise as well as mixed gaussian and impulse noise

fuzzy peer groups for reducing mixed gaussian impulse noise from color images the peer group of an image pixel is a pixel similarity based concept which has been successfully used to devise image denoising methods however since it is difficult to define the pixel similarity in a crisp way we propose to represent this similarity in fuzzy terms in this paper we introduce the fuzzy peer group concept which extends the peer group concept in the fuzzy setting a fuzzy peer group will be defined as a fuzzy set that takes a peer group as support set and where the membership degree of each peer group member will be given by its fuzzy similarity with respect to the pixel under processing the fuzzy peer group of each image pixel will be determined by means of a novel fuzzy logic based procedure we use the fuzzy peer group concept to design a two step color image filter cascading a fuzzy rule based switching impulse noise filter by a fuzzy average filtering over the fuzzy peer group both steps use the same fuzzy peer group which leads to computational savings the proposed filter is able to efficiently suppress both gaussian noise and impulse noise as well as mixed gaussian impulse noise experimental results are provided to show that the proposed filter achieves a promising performance

the influence of some fuzzy implication operators on the accuracy of a fuzzy model part ii abstract the influence of fuzzy implication operators and the connective also on the accuracy of a fuzzy model of a d c series motor is considered some typical fuzzy implication operators are applied to the construction of a fuzzy model of a d c series motor a root mean square error is used as the criterion of the fuzzy model s adequacy to the real system a number of mathematical operations necessary for the implementation of the fuzzy model are used as the criterion by which the fuzzy model s applicability if estimated from the point of view of computing techniques the best types of fuzzy relations representing fuzzy models of a real system are chosen in order to secure the least root mean square error with minimal number of mathematical operations necessary for computer implementation

continuity issues of the implicational interpretation of fuzzy rules the implicational interpretation of fuzzy rules has received little attention in real world applications so far this is largely due to the fact that ensuring continuity of the resulting function is not a straightforward task this paper targets this subject departing from consistent linguistic descriptions rule bases we introduce sufficient conditions for the continuity of the implicational interpretation with mean of maximum defuzzification we demonstrate that continuity can be achieved under practically feasible conditions regardless of the dimensionality of the input

using uncorrupted neighborhoods of the pixels for impulsive noise suppression with anfis in this paper a novel adaptive network based fuzzy inference system anfis based filter abf is presented for the restoration of images corrupted by impulsive noise in the abf is performed in two steps in the first step impulse detection is realized by using statistical tools in the second step a nonlinear filtering scheme based on anfis is performed for only the corrupted pixels detected in the first step to demonstrate the effectivity of abf at the removal of high level in extensive simulations were realized for abf and nine different comparison filters empirical results indicate that the proposed filter achieves a better performance than the comparison filters in terms of noise suppression and detail preservation even when the images are highly corrupted by in

a theoretical study of the brinell hardness test brinell tests have long been the preferred method of assaying the hardness of metals during forming operations the general significance of the test has been codified in empirical laws especially those of meyer o neill and tabor on the other hand the indentation of elastoplastic media by a ball has never been thoroughly analysed in the context of modern mechanics of continua this is the objective here the actual boundary value problem is non steady but can be made steady in terms of reduced variables when the material response is suitably modelled namely the strain should be infinitesimal and expressible as the tensor gradient of a potential function of the stress deviator the function must be homogeneous of degree n 1 2 but is otherwise arbitrary meyer s law is then derivable rigorously ahead of a detailed solution moreover the predicted index is 2n 1 n substantiating o neill s rule for materials whose strain under uniaxial tension varies as some nth power of the stress it is predicted also that the piling up or sinking in around the indenter is correlated with n in the manner observed these immediate implications of the model amount to a priori evidence of its overall ability to simulate elastoplastic response of the kind induced in brinell tests evidence a posteriori was supplied by finite element computations for a standard potential whose level surfaces are of mises type mixed nine node quadrilateral elements were adopted these are known to promote optimal convergence and are well suited to handling incompressibility a carefully graded mesh provided about 24000 degrees of freedom computations were performed for n 1 2 4 and 10 covering the practical range the results include i distributions of the contact pressure and the radial and circumferential in surface stresses ii profiles of the deformed surface and iii contours of representative strain in the main body of material excellent agreement was obtained with tabor s experimental findings that the representative strain at the contact perimeter is y0 4 a d for any n a is the contact radius and d the ball diameter while the average pressure is 2 8 times the flow stress at strain y in a tension test finally strain paths at fixed stations were sampled to check the simulation of elastoplastic response locally as well as overall

a study on the parallel computability of multi fold approximate reasoning abstract in an approximate reasoning on multiple fuzzy rules the inference result of fati first aggregation then inference is more specific than that of fita first inference then aggregation in other words fita gives a more vague inference result than fati from the viewpoint of less vagueness of inference result fati is more preferable than fita however from the viewpoint of computing speed fita is more preferable than fati because fita makes parallel computing possible in this paper we develop an approximate reasoning method that the inference result based on fita is equal to the one of fati for this purpose first a certain approximate reasoning environment with sup t norm composition godel implication min combination and normal partition of premise and consequent in fuzzy rules is set up and then the condition of an observed fact that satisfies fati fita is investigated next we give the algorithm that satisfies fati fita for the other observed facts

indentation of a power law creeping solid the aim of this paper is to establish a rigorous theoretical basis for interpreting the results of hardness tests on creeping specimens we investigate the deformation of a creeping half space with uniaxial stress strain behaviour 0 0 m which is indented by a rigid punch both axisymmetric and plane indenters are considered the shape of the punch is described by a general expression which includes most indenter profiles of practical importance two methods are used to solve the problem the main results are found using a transformation method suggested by r hill it is shown that the creep indentation problem may be reduced to a form which is independent of the geometry of the punch and depends only on the material properties through m the reduced problem consists of a nonlinear elastic half space which is indented to a unit depth by a rigid flat punch of unit radius in the axisymmetric case or unit semi width in the plane case exact solutions are given for m 1 and m for m between these two limits the reduced problem has been solved using the finite element method the results enable the load on the indenter and the contact radius to be calculated in terms of the indentation depth and rate of penetration the stress strain and displacement fields in the half space may also be deduced the accuracy of the solution is demonstrated by comparing the results with full field finite element calculations the predictions of the theory are shown to be consistent with experimental observations of hardness tests on creeping materials reported in the literature

a new adaptive center weighted median filter for suppressing impulsive noise in images in this work a new adaptive center weighted median acwm filter is proposed for improving the performance of median based filters preserving image details while effectively suppressing impulsive noise the proposed filter is an adaptive cwm filter with an adjustable central weight obtained by partitioning the observation vector space to obtain the optimal weight for each block the efficient scalar quantization sq method is used to partition the observation vector space the center weight within each block is obtained by using a learning approach based on the least mean square lms algorithm the noise filtering procedure is progressively applied through several iterations so that the mean square error of the output can be minimized experimental results have demonstrated that the proposed filter outperforms many well accepted median based filters in terms of both noise suppression and detail preservation the proposed new filter also provides excellent robustness at various percentages of impulsive noise

composite quantile regression for the receiver operating characteristic curve the covariate speciflc receiver operating characteristic curve is frequently used to evaluate the classiflcation accuracy of a diagnostic test when it is associated with certain covariates in this paper we proposed a new procedure for estimating this curve based on a reformulation of the conventional location scale model as well as the idea of composite quantile regression asymptotic normality of the proposed estimators is established both for the regression parameters and the covariate speciflc receiver operating characteristic curve at a flxed false positive point simulation results show that the new estimators compare favorably to their main competitors in terms of the standard error we apply the new procedure to data from the national alzheimer s coordinating center

optimization of linear problems subjected to the intersection of two fuzzy relational inequalities defined by dubois prade family of t norms abstract in this paper optimization of a linear objective function with fuzzy relational inequality constraints is investigated in doing so the feasible region is formed by the intersection of two inequality fuzzy systems and dubois prade family of t norms which are considered as fuzzy composition the most well known continuous t norms are archimedean such as frank yager hamacher sugeno weber and schweizer sklar family an interesting family of t norms that is not archimedean has been introduced by dubois and prade in this paper the resolution of the feasible region of the problem is initialy investigated when it is defined with max duboise prade composition a necessary and sufficient condition along with three other necessary conditions are derived for determining the feasibility of the problem moreover two procedures have also been presented with the aim of simplifying the current linear problems a method is proposed to generate random feasible max dubois prade fuzzy relational inequalities and an algorithm is accordingly presented to solve the problem finally an example is described to illustrate this algorithm

a fuzzy impulse noise detection and reduction method removing or reducing impulse noise is a very active research area in image processing in this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise fuzzy impulse noise detection and reduction method fidrm it can also be applied to images having a mixture of impulse noise and other types of noise the result is an image quasi without or with very little impulse noise so that other filters can be used afterwards this nonlinear filtering technique contains two separated steps an impulse noise detection step and a reduction step that preserves edge sharpness based on the concept of fuzzy gradient values our detection method constructs a fuzzy set impulse noise this fuzzy set is represented by a membership function that will be used by the filtering method which is a fuzzy averaging of neighboring pixels experimental results show that fidrm provides a significant improvement on other existing filters fidrm is not only very fast but also very effective for reducing little as well as very high impulse noise

a fuzzy noise reduction method for color images a new fuzzy filter is presented for the reduction of additive noise for digital color images the filter consists of two subfilters the first subfilter computes fuzzy distances between the color components of the central pixel and its neighborhood these distances determine in what degree each component should be corrected all performed corrections preserve the color component distances the goal of the second subfilter is to correct the pixels where the color components differences are corrupted so much that they appear as outliers in comparison to their environment experimental results show the feasibility of the proposed approach we compare with other noise reduction methods by numerical measures and visual observations we also illustrate the performance of the proposed method as preprocessing step for edge detection

interpolative reasoning with insufficient evidence in sparse fuzzy rule bases abstract rule based fuzzy approximate reasoning uses various techniques of modified modus ponens the observation is in most cases not identical with any of the antecedents in the rules however a conclusion still can be computed by using some combination of all consequents where an overlapping of observation and antecedent is present if the rule base is sparse i e it contains insufficient information on the total state space it might occur that an observation has absolutely no overlapping with any of the antecedents and so not even a single rule is fired i e no conclusion can be computed on the basis of modus ponens in such a case interpolative reasoning in the strict sense can be applied some kind of weighted average of the flanking rules is calculated this technique can be extended to a form of extrapolation when the observation is not flanked from both sides linear interpolation and extrapolation is presented and then the idea is extended to arbitrary approximation

genetic based fuzzy hybrid multichannel filters for color image restoration abstract on the design of multichannel filters especially in color image restoration it is not easy to simultaneously achieve three objectives noise attenuation chromaticity retention and edges or details preservation in this paper we propose a new class of multichannel filters called genetic based fuzzy hybrid multichannel gfhm filters to reach these three objectives simultaneously the design of gfhm filters is mainly based on human concept heuristic rules and genetic algorithms because the human concept can be readily and efficiently expressed by fuzzy implicative rules gfhm filters can take the useful characteristics of filtering behavior of three filters a vector median a vector directional and an identity filter since genetic algorithms possess the global searching capability for an optimal solution they are able to effectively optimize gfhm filters to improve the filtering performance in color image restoration applications extensive simulation results illustrate that gfhm filters not only achieve these three objectives but also possess the robust and the adaptive capability moreover these simulation results also demonstrate that the performance of gfhm filters outperforms that of other proposed filtering techniques

semiparametric receiver operating characteristic analysis to evaluate biomarkers for disease the receiver operating characteristic roc curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary various methodologies for estimating and comparing roc curves have been developed one approach due to pepe uses a parametric regression model rocx u g h0 u 0x with the baseline function h0 u specified up to a finite dimensional parameter in this article we extend the regression models by allowing arbitrary nonparametric baseline functions we also provide asymptotic distribution theory and procedures for making statistical inference we illustrate our approach with dataset from a prostate cancer biomarker study simulation studies suggest that the extra flexibility inherent in the semiparametric method is gained with little loss in statistical efficiency

fuzzy two step filter for impulse noise reduction from color images a new framework for reducing impulse noise from digital color images is presented in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique we call this filter the fuzzy two step color filter the fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning this phase determines three separate membership functions that are passed to the filtering step these membership functions will be used as a representation of the fuzzy set impulse noise one function for each color component our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image

a regression modelling framework for receiver operating characteristic curves in medical diagnostic testing summary receiver operating characteristic curves roc s are used to evaluate diagnostic tests when test results are not binary they describe the inherent capacity of the test for distinguishing between truly diseased and nondiseased subjects although methodology for estimating and for comparing roc s is well developed to date no general framework exists for evaluating covariate effects on roc s we formulate a general regression model which allows the effects of covariates on test accuracy to be succinctly summarised such covariates might include for example characteristics of the patient or test environment test type or severity of disease the regression models are shown to arise naturally from some classic models for continuous or ordinal test data regression parameters are fitted using an estimating equation approach the method is illustrated on data from a study of multiformat photographic images used for scintigraphy

similarity analysis of creep indentation tests the general theory of axisymmetric hardness tests on nonlinear media is approached from the standpoint of similarity transformations it is shown how an entire process of indentation can be made to depend on the solution of just one boundary value problem in scaled variables and with a fixed geometry once this single auxiliary solution has been obtained the values of all physical quantities in the original problem can be generated readily at any stage without further numerical error even by themselves the similarity relations provide valuable information about for example an invariant connection between the depth of penetration and the radius of contact or about the variation of penetration with time in a creep test under dead load two kinds of material behaviour are considered a nonlinear elastic modelling strain hardening plasticity and b nonlinear viscous modelling secondary creep in either category the constitutive specification is sufficiently flexible to represent a wide range of actual responses in the context of hardness testing the analysis for case a extends a theory of ball indentation by hill et al to a class of indenters with shapes varying from flat to conical it also prepares the ground for case b which is more difficult and calls for a quite different auxiliary problem

semiparametric estimation of the covariate specific roc curve in presence of ignorable verification bias covariate specific roc curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker when the accuracy of the test is associated with certain covariates in many large scale screening tests the gold standard is subject to missingness due to high cost or harmfulness to the patient in this paper we propose a semiparametric estimation of the covariate specific roc curves with a partial missing gold standard a location scale model is constructed for the test result to model the covariates effect but the residual distributions are left unspecified thus the baseline and link functions of the roc curve both have flexible shapes with the gold standard missing at random mar assumption we consider weighted estimating equations for the location scale parameters and weighted kernel estimating equations for the residual distributions three roc curve estimators are proposed and compared namely imputation based inverse probability weighted and doubly robust estimators we derive the asymptotic normality of the estimated roc curve as well as the analytical form the standard error estimator the proposed method is motivated and applied to the data in an alzheimer s disease research

theory of expertons and fuzzy logic abstract since 1965 when l a zadeh presented the concept of fuzzy sets various types of extensions have been proposed the treatment of subjectivity knowledge and uncertain data from one or several experts is now an important field in computer science in this paper a general concept the experton is described it is especially adapted for the case where several experts and or users are concerned the properties of expertons give also a monoid and a distributive lattice with the norms min and max then every process used with fuzzy sets stays available for expertons with a constraint of monotonicity expertons are based on probabilistic sets when intervals of probabilities are considered

geometric features based filtering for suppression of impulse noise in color images a geometric features based filtering technique named as the adaptive geometric features based filtering technique agff is presented for removal of impulse noise in corrupted color images in contrast with the traditional noise detection techniques where only 1d statistical information is used for noise detection and estimation a novel noise detection method is proposed based on geometric characteristics and features i e the 2 d information of the corrupted pixel or the pixel region leading to effective and efficient noise detection and estimation outcomes a progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise extensive experiments conducted using a wide range of test color images have shown that the agff is superior to a number of existing well known benchmark techniques in terms of standard image restoration performance criteria including objective measurements the visual image quality and the computational complexity

an interpretation for the roc curve and inference using glm procedures the accuracy of a medical diagnostic test is often summarized in a receiver operating characteristic roc curve this paper puts forth an interpretation for each point on the roc curve as being a conditional probability of a test result from a random diseased subject exceeding that from a random nondiseased subject this interpretation gives rise to new methods for making inference about roc curves it is shown that inference can be achieved with binary regression techniques applied to indicator variables constructed from pairs of test results one component of the pair being from a diseased subject and the other from a nondiseased subject within the generalized linear model glm binary regression framework roc curves can be estimated and we highlight a new semiparametric estimator covariate effects can also be evaluated with the glm models the methodology is applied to a pancreatic cancer dataset where we use the regression framework to compare two different serum biomarkers asymptotic distribution theory is developed to facilitate inference and to provide insight into factors influencing variability of estimated model parameters

composite quantile regression and the oracle model selection theory coefficient estimation and variable selection in multiple linear regression is routinely done in the penalized least squares ls framework the concept of model selection oracle introduced by fan and li j amer statist assoc 96 2001 1348 1360 characterizes the optimal behavior of a model selection procedure however the least squares oracle theory breaks down if the error variance is infinite in the current paper we propose a new regression method called composite quantile regression cqr we show that the oracle model selection theory using the cqr oracle works beautifully even when the error variance is infinite we develop a new oracular procedure to achieve the optimal properties of the cqr oracle when the error variance is finite cqr still enjoys great advantages in terms of estimation efficiency we show that the relative efficiency of cqr compared to the least squares is greater than 70 regardless the error distribution moreover cqr could be much more efficient and sometimes arbitrarily more efficient than the least squares the same conclusions hold when comparing a cqr oracular estimator with a ls oracular estimator

an approach to theory of fuzzy signals basic definitions the paper presents a new approach to the description of fourier transform and correlation function taking into consideration the uncertainty of the transformed function a conventional approach to uncertainty employs probabilistic description here fuzzy logic theory is applied to describe this uncertainty new definitions namely fuzzy fourier transform fuzzy correlation and possibility correlation are proposed the definition of correlation function is based on the concept of fuzzy functional spaces some examples of applications and a comparison with the conventional approach are shown

asynchronous filtering of nonlinear markov jump systems with randomly occurred quantization via t s fuzzy models this paper focuses on the dissipativity based asynchronous filtering problem for a class of discrete time takagi sugeno fuzzy markov jump systems subject to randomly occurred quantization considering the random fluctuations of network conditions the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way to take full advantage of the partial information of system modes for the desired system performance we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous the mode dependent time varying delays are introduced which have different bounds for different system modes via fuzzy mode dependent lyapunov functional approach that can reduce conservatism a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly mathcal q mathcal s mathcal r dissipative then the gains of the filter are obtained by solving a set of linear matrix inequalities lmis an example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices delays quantization parameter and the degree of asynchronous jumps are given

interval valued fuzzy sets representation on multiple antecedent fuzzy s implications and reasoning abstract interval valued fuzzy sets are used to represent multiple antecedent s implications based on normal forms where linguistic connectives antecedents and consequents are assumed to be fuzzy these intervals are represented equivalently with their lower and upper bounds general forms are given for the bounds of multiple antecedent fuzzy s implications bounds for multiple antecedent reasoning are also investigated based on the bounds of multiple antecedent s implications it is suggested that modification functions utilized in approximation analogical reasoning aar be determined in such a way that consequents deduced by aar fall within the bounds of the reasoning an example is given to show how the bounds might be used to choose a modification scheme in aar

comparison of fuzzy reasoning methods l a zadeh e h mamdani and m mizumoto et al have proposed methods for fuzzy reasoning in which the antecedent involves a fuzzy conditional proposition if x is a then y is b with a and b being fuzzy concepts mizumoto et al have investigated the properties of their methods in the case of generalized modus ponens this paper deals with the properties of their methods in the case of generalized modus tollens and investigates the other new fuzzy reasoning methods obtained by introducing the implication rules of many valued logic systems finally the properties of syllogism and contrapositive are investigated under each fuzzy reasoning method

partition fuzzy median filter based on fuzzy rules for image restoration in this paper a novel adaptive median based filter called the partition fuzzy median pfm filter is proposed for improving the median based filter to preserve image details while effectively suppressing impulsive noises the proposed filter achieves its effect through a summation of the weighted output of the median filter and the related weighted input signal the weights are set in accordance with the fuzzy rules in order to design this weight function a method to partition of the observation vector space and a learning approach are proposed so that the mean square error of the filter output can be minimum based on the constrained least mean square algorithm an iterative learning procedure is derived and its convergence property is investigated as for the noise suppressing on both fixed and random valued impulses without degrading the quality of fine details extensive experimental results demonstrate that the proposed filter outperforms the other median based filters in the literature the new filter also provides excellent robustness with respect to various percentages of impulse noise in our testing examples

control functions of fuzzy controllers abstract several interesting analytic and geometric properties of the fuzzy controllers are discussed from these discussions some kind of measure of nonlinearity of the fuzzy controllers is proposed a detailed study of the geometric structures of the fuzzy control functions is also carried out and the result is compared with that of linear control

analysis of the properties of median and weighted median filters using threshold logic and stack filter representation the deterministic properties of weighted median wm filters are analyzed threshold decomposition and the stacking property together establish a unique relationship between integer and binary domain filtering the authors present a method to find the weighted median filter which is equivalent to a stack filter defined by a positive boolean function because the cascade of wm filters can always be expressed as a single stack filter this allows expression of the cascade of wm filters as a single wm filter a direct application is the computation of the output distribution of a cascade of wm filters the same method is used to find a nonrecursive expansion of a recursive wm filter as applications of theoretical results several interesting deterministic and statistical properties of wm filters are derived

a new model of fuzzy reasoning abstract a new model of fuzzy reasoning is proposed a comparison of inference results using the new model and the linguistic synthesis approach is given

mixed finite element methods reduced and selective integration techniques a unification of concepts the equivalence of certain classes of mixed finite element methods with displacement methods which employ reduced and selective integration techniques is established this enables one to obtain the accuracy of the mixed formulation without incurring the additional computational expense engendered by the auxiliary field of the mixed method applications and numerical examples are presented for problems with constraints which can be difficult to enforce in finite element approximations and have often dictated the use of mixed principles these include thin beams and plates and linear and nonlinear incompressible and nearly incompressible continuum problems in solid and fluid mechanics

representation of digital image by fuzzy neural network a multilayer feedforward fuzzy neural network fnn by which the predetermined fuzzy system can be realized is constructed to express a given two dimensional 2 d digital image it is shown that such a network is universal approximator the fnn approach provides us with the representation model of the 2 d discrete image in noise environment some given fuzzy numbers are employed to describe gray levels of a digital image and the deviation of corrupted image to its noise free one a class of fuzzy rules for removing impulse noise in degraded image are presented the corresponding fnn may work as a filter which improves the performances of median and rank conditioned rank selection filters especially such a filter may to a greatest extent maintain the fine structure of the image under the minimum mean absolute error criterion the membership functions of fuzzy numbers are adaptively adjusted and an optimal fnn for image representation is derived in noise environment simultaneously some real image examples are systemically analyzed to show that the fnn may result in higher quality global restoration

cdna microarray image processing using fuzzy vector filtering framework this paper presents a novel filtering framework capable of processing cdna microarray images the proposed two component adaptive vector filters integrate well known concepts from the areas of fuzzy set theory nonlinear filtering multidimensional scaling and robust order statistics by appropriately setting the weighting coefficients in a generalized framework the method is capable of removing noise impairments while preserving structural information in cdna microarray images noise removal is performed by tuning a membership function which utilizes distance criteria applied to cdna vectorial inputs at each image location the classical vector representation adopted here for a two channel processing task as well as a new color ratio model representation are used simulation studies reported in this paper indicate that the proposed adaptive fuzzy vector filters are computationally attractive yield excellent performance and are able to preserve structural information while efficiently suppressing noise in cdna microarray data

handling the nonlinearity of a fuzzy logic controller at the transition between rules abstract a fuzzy logic controller approximates a desired control surface by using the outputs of its fuzzy control rules the shape of this control surface is mainly influenced by the linguistic variables of the rule base the basic operators of the fuzzy sets the implication the inference and the defuzzification method the linearity nonlinearity of this control surface is discussed in many studies however none of those studies has explicitly considered the linearity nonlinearity at the transition between fuzzy logic rules in this paper a simple approach to control the linearity nonlinearity at this transition by a modified center of gravity defuzzification method is proposed namely by introducing the so called defuzzification weights to the overlapping areas of the consequent those defuzzification weights can be determined heuristically or automatically both cases will be demonstrated for the latter a feedforward neural network is employed