componentwise decomposition of some lattice valued fuzzy integrals lattice valued fuzzy measures are lattice valued set functions which assign the bottom element of the lattice to the empty set the top element of the lattice to the entire universe and satisfy the property of monotonicity if the lattice is complete then a lattice valued fuzzy integral of sugeno type with similar properties such as the sugeno integral in its original form can be introduced in a natural way the main result of the paper is a componentwise decomposition theorem of an l valued fuzzy integral to its l valued fuzzy integrals components where l is a complete lattice with negation and l a b a email protected l a email protected is organized as a complete lattice too this result is useful to obtain the properties of l valued fuzzy integrals from the properties of l valued fuzzy integrals and to calculate in a simple way the values of some integrals from the values of the components the important case l 0 1 when l becomes the lattice of the intuitionistic fuzzy values is distinctly discussed an idea of application to synthetic evaluation of objects is also suggested

measures of fuzzy sets and measures of fuzziness abstract the measures presented in this paper are defined by using weber s concept of decomposable measures m of crisp sets having in particular the archimedean decomposable operations in view section 2 measures m of fuzzy sets are introduced as integrals with respect to m for the archimedean cases weber s integral will be used as alternative to sugeno s and choquet s concepts section 3 what fuzziness means will be described by functions of fuzziness f another name entropy n functions with respect to a negation in addition to the types of functions of fuzziness which are induced by concave functions we discuss also the ones which are induced by fuzzy connectives section 4 now using m for measuring the importance of items and f for the fuzziness of the possible values of a fuzzy set m f serves us as a measure of the fuzziness f of the concepts of de luca and termini capocelli and de luca kaufmann knopfmacher loo gottwald dombi and under the restriction to the archimedean cases also the concepts of trillas and riera and yager turn out to be special cases section 5

semiconormed possibility integrals and multi feature pattern classification abstract the semiconormed possibility integrals are proposed as a multi feature pattern classification model a semiconormed possibility integral is a nonlinear integration of a function and its corresponding non normalized possibility measures over feature space the function of an object s feature vector represents the possibilities with uncertainty that the object belongs to a class the uncertainty is due to the similar characteristics of objects from different classes and the distortion of the original characteristic information caused by feature data acquisition systems the uncertainty is assessed by the non normalized possibility measures a possibility measure of a feature is considered as the credibility of the feature to provide reliable information for pattern classification integration of a function and the possibility measures effectively reduces the uncertainty and improves the pattern classification results a pattern classification algorithm based on the semiconormed possibility integrals was used to classify a set of ellipse data and the well known iris data the classification results were compared with those obtained by using bayes classifier

on fuzzy measures defined by seminormed fuzzy integrals and semiconormed fuzzy integrals abstract in this paper we show that for the continuous t seminorms and continuous t semiconorms the properties of null addivity pseudo null addivity autocontinuity and pseudo autocontinuity of fuzzy measures g can be preserved by the fuzzy measures g h and g h defined by seminormed fuzzy integrals and semiconormed fuzzy integrals respectively at most under the conditions that x y 0 x y 0 and x y x y g can be preserved by the fuzzy measures g h under the condition that x y 0 x y 0

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

measures of fuzziness of fuzzy events abstract we prove some new results on two families of fuzzy integrals defined in a previous paper and by means of them we obtain entropy measures of fuzzy sets not necessarily finite which contain as particular cases the measures of batle and trillas and weber we define another measure of fuzziness which is a function of the previous ones and satisfies with slight restrictions de luca and termini s axioms

estimation of resonant frequencies and quality factors from time domain computations a new frequency domain subspace algorithm for the estimation of resonant frequencies and quality factors given the output from time domain computations is developed and tested it applies the discrete fourier transform to the time domain signal and the estimates are computed from the frequency data within the frequency band of interest thus out of band disturbances are suppressed through a band pass filtering operation in frequency domain which is free from the transient effects that are inevitable for standard time domain filtering techniques we also propose an efficient procedure to generate short time domain excitations with an a priori specified frequency spectrum by combining such excitations with the frequency domain subspace algorithm we estimated to high accuracy the lowest 167 resonant frequencies of a three dimensional bow tie microwave resonator given two separate time domain computations with in total 23 000 time steps a free space computation for a cavity with an aperture shows that the estimation algorithm can be used to efficiently and accurately extrapolate time domain signals the tests show that it is possible to reduce the overall computation time by several orders of magnitude for systems with undamped or weakly damped modes it operates as a post processing step and thus decouples the estimation of frequencies and amplitudes from the time domain solver which offers a considerable flexibility

homogeneity properties of seminormed fuzzy integrals this paper is concerned with the maxitivity and homogeneity of integrals in a very general setting for the semi co normed fuzzy integrals we study the maxitivity minitivity and homogeneity homogeneity properties some necessary conditions for the fuzzy integrals to have such homogeneity properties are obtained

on the monotone convergence theorem for conormed seminormed fuzzy integral abstract in this paper under the condition that the t conorm and t seminorm are continuous we prove the monotone convergence theorem of conormed seminormed fuzzy integral for non decreasing monotone measurable function sequence also we show that the result cannot be extended to the case of the non increasing function sequence

esprit estimation of signal parameters via rotational invariance techniques an approach to the general problem of signal parameter estimation is described the algorithm differs from its predecessor in that a total least squares rather than a standard least squares criterion is used although discussed in the context of direction of arrival estimation esprit can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise it exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure the technique when applicable manifests significant performance and computational advantages over previous algorithms such as mem capon s mlm and music

a fourth order accurate embedded boundary method for the wave equation a fourth order accurate embedded boundary method for the scalar wave equation with dirichlet or neumann boundary conditions is described the method is based on a compact pade type discretization of spatial derivatives together with a taylor series method modified equation in time a novel approach for enforcing boundary conditions is introduced which uses interior boundary points instead of exterior ghost points this technique removes the small cell stiffness problem for both dirichlet and neumann boundary conditions is more accurate and robust than previous methods based on exterior ghost points and guarantees that the solution is single valued when slender bodies are treated numerical experiments are presented to illustrate the stability and accuracy of the method as well as its application to problems with complex geometries

electromagnetic transmission through multiple circular apertures in a thick conducting plane the boundary value problem of electromagnetic wave scattering from multiple circular apertures in a thick conducting plane is rigorously solved the eigenfunction expansion integral transform and superposition principle are utilized to represent the scattered field in the discrete and continuous modes the boundary conditions are enforced to obtain a set of simultaneous equations for the discrete modal coefficients the transmission coefficient is represented in a fast convergent series computation is performed to illustrate the behavior of transmission and coupling through multiple circular apertures in terms of the aperture geometry

subspace based multivariable system identification from frequency response data two noniterative subspace based algorithms which identify linear time invariant mimo multi input multioutput systems from frequency response data are presented the algorithms are related to the recent time domain subspace identification techniques the first algorithm uses equidistantly in frequency spaced data and is strongly consistent under weak noise assumptions the second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure

arma based time signature estimator for analyzing resonant structures by the fdtd method we propose an algorithm for estimation of the optimal system parameters of time sequences tss computed by the finite difference time domain fdtd method with the goal of accurate representation of the time signature using low order models the fdtd method requires computation of very long time sequences to accurately characterize the slowly decaying transient behavior of resonant structures therefore it becomes critical to investigate methods of reducing the computational time for such objects several researchers have argued that the fdtd ts can be modeled as the impulse response ir of an autoregressive moving average arma transfer function however it is known that determination of arma parameters by ir matching is a complex nonlinear optimization problem hence many existing methods in em literature tend to use prony based linear predictor type spectrum estimation algorithms which minimize a linearized equation error criterion that approximates the true nonlinear model fitting error criterion as a result significantly high model orders are needed by these methods to achieve good corroboration in the frequency domain especially when a magnitude spectrum has deep nulls or notches we propose to use a deterministic arma approach which minimizes the true nonlinear criterion iteratively and attains significantly improved ir fit over prony s 1795 method using fewer arma model parameters for a given time sequence of an analyzed circuit the issues of model order selection and choice of decimation factor are also addressed systematically the improved performance of the proposed algorithm is demonstrated with transient simulation and signal analysis of microstrip structures which manifest deep nulls in the frequency domain

possibility and necessity integrals abstract in this paper we introduce seminormed and semiconormed fuzzy integrals associated with confidence measures these confidence measures have a field of sets as their domain and a complete lattice as their codomain in introducing these integrals the analogy with the classical introduction of lebesgue integrals is explored and exploited it is amongst other things shown that our integrals are the most general integrals that satisfy a number of natural basic properties in this way our dual classes of fuzzy integrals constitute a significant generalization of sugeno s fuzzy integrals a large number of important general properties of these integrals is studied furthermore and most importantly the combination of seminormed fuzzy integrals and possibility measures on the one hand and semiconormed fuzzy integrals and necessity measures on the other hand is extensively studied it is shown that these combinations are very natural and have properties which are analogous to the combination of lebesgue integrals and classical measures using these results the very basis is laid for a unifying measure and integral theoretic account of possibility and necessity theory in very much the same way as the theory of lebesgue integration provides a proper framework for a unifying and formal account of probability theory

unitary esprit how to obtain increased estimation accuracy with a reduced computational burden esprit is a high resolution signal parameter estimation technique based on the translational invariance structure of a sensor array previous esprit algorithms do not use the fact that the operator representing the phase delays between the two subarrays is unitary the authors present a simple and efficient method to constrain the estimated phase factors to the unit circle if centro symmetric array configurations are used unitary esprit the resulting closed form algorithm has an esprit like structure except for the fact that it is formulated in terms of real valued computations throughout since the dimension of the matrices is not increased this completely real valued algorithm achieves a substantial reduction of the computational complexity furthermore unitary esprit incorporates forward backward averaging leading to an improved performance compared to the standard esprit algorithm especially for correlated source signals like standard esprit unitary esprit offers an inexpensive possibility to reconstruct the impinging wavefronts signal copy these signal estimates are more accurate since unitary esprit improves the underlying signal subspace estimates simulations confirm that even for uncorrelated signals the standard esprit algorithm needs twice the number of snapshots to achieve a precision comparable to that of unitary esprit thus unitary esprit provides increased estimation accuracy with a reduced computational burden

time domain finite element methods various time domain finite element methods for the simulation of transient electromagnetic wave phenomena are discussed detailed descriptions of test trial spaces explicit and implicit formulations nodal and edge facet element basis functions are given along with the numerical stability properties of the different methods the advantages and disadvantages of mass lumping are examined finally the various formulations are compared on the basis of their numerical dispersion performance

analysis of coupling between array of open ended coaxial lines in an infinite ground plane the problem of coupling through an open ended coaxial line array in a conducting plane is solved using the integral transform superposition principle and eigenfunction expansions computation is performed to illustrate the coupling behavior of coaxial line array our theory agrees well with other simulation results

fuzzy sets and their operations ii among the basic operations which can be performed on fuzzy sets are the operations of union intersection complement algebraic product and algebraic sum in addition to these operations new operations called bounded sum and bounded difference were defined by l a zadeh to investigate the fuzzy reasoning which provides a way of dealing with the reasoning problems which are too complex for precise solution this paper investigates the algebraic properties of fuzzy sets under these nex operations of bounded sum and bounded difference and the properties of fuzzy sets in the case where these new operations are combined with the well known operations of union intersection algebraic product and algebraic sum

characterization and comparison of sugeno and choquet integrals abstract starting from the concept of equiordered functions we characterize two types of fuzzy integrals that can be defined on any kind of fuzzy measure sugeno and choquet integrals the characterization theorems that we obtain allow us to carry out a comparative study of these two fuzzy integrals pointing out their formal similarities and their conceptual differences

additions of interactive fuzzy numbers this paper provides an account of an approach to modeling unknown data by means of fuzzy set theory and addresses the problem of deriving the uncertainty on a sum of variables whose values lie within fuzzy intervals the first part is an extensive presentation of the theoretical background of the approach the extension principle is stated in terms of possibility of an event the concept of variable interaction is investigated at length section ii gives new results regarding the effective practical computation of additions of fuzzy numbers its originality lies in the introduction of interaction which enables to control the growth of uncertainty in calculations moreover the problem of computing mathematical expectations with fuzzy probabilities is solved the results derived in this paper can easily be used in decision problems where values of parameters or decision variables are not yet precisely fixed or assessed typical applications could be multicriteria optimization and decision making under uncertainty where fuzzy expected utilities can be obtained out of uncompletely assessed probabilities more generally fuzzy arithmetic can be an important tool for sophisticated computationally tractable sensitivity analysis in systems modeling computer aided design and operations research

problems of classification in a fuzzy environment this paper presents a classification model in a fuzzy environment by using possibility distributions and fuzzy measures optimal in bayes sense membership functions are induced these functions are similar to decision rules used in classical discriminant analysis the relations between fuzzy measures and possibility measures are also discussed to formulate a classification model where sugeno s fuzzy measures and integrals are used

two families of fuzzy integrals abstract in this paper we present two definitions of fuzzy integrals arising from the t seminorm and the t semiconorm concepts we show that sugeno s fuzzy integral is a particular case of them and we study their principal properties finally we suggest some applications of these results

subspace model identification part 1 the output error state space model identification class of algorithms in this paper we present two novel algorithms to realize a finite dimensional linear time invariant state space model from input output data the algorithms have a number of common features they are classified as one of the subspace model identification schemes in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input output data this structure is then exploited in the calculation of a realization another common feature is their algorithmic organization an rq factorization followed by a singular value decomposition and the solution of an overdetermined set or sets of equations the schemes assume that the underlying system has an output error structure and that a measurable input sequence is available the latter characteristic indicates that both schemes are versions of the mimo output error state space model identification moesp approach the first algorithm is denoted in particular as the elementary moesp scheme

subspace based methods for the identification of linear time invariant systems subspace based methods for system identification have attracted much attention during the past few years this interest is due to the ability of providing accurate state space models for multivariable linear systems directly from input output data the methods have their origin in classical state space realization theory as developed in the 1960s the main computational tools are the qr and the singular value decompositions here an overview of existing subspace based techniques for system identification is given the methods are grouped into the classes of realization based and direct techniques similarities between different algorithms are pointed out and their applicability is commented upon we also discuss some recent ideas for improving and extending the methods a simulation example is included for comparing different algorithms the subspace based approach is found to perform competitive with respect to prediction error methods provided the system is properly excited

far field time domain calculation from aperture radiators using the fdtd method it is often desirable to determine the far field radiation pattern from an aperture in a metal plate the use of time domain methods like the finite difference time domain fdtd method is problematic because of the necessity of modeling the entire space of interest this is often circumvented by using the equivalence principle to determine the far field by equivalent currents generated in the near field however these transformations are often mathematically complex this paper presents an extremely simple formulation for calculating the time domain fields from some types of aperture radiators by using wavelet analysis the pertinent parameters can be compressed and stored to be used later to resynthesize the radiated fields

λ generalized sugeno integral and its application sugeno integral is a useful tool for many problems in several domains especially in game theory measure theory decision making probability image processing and finance in this paper we introduce the concept of generalized sugeno integral then we establish general versions of chebyshev type inequality for this kind of sugeno integral by using a monotone measure our results generalize and improve the corresponding ones in the literature finally some conclusions and problems for further investigations are given

further discussions on convergence theorems for seminormed fuzzy integrals and semiconormed fuzzy integrals abstract in this paper a convergence in fuzzy measure resp pseudo in fuzzy measure theorem for seminormed resp semiconormed fuzzy integrals is proved provided that the fuzzy measure is autocontinuous resp pseudo autocontinuous furthermore an almost everywhere resp pseudo almost everywhere convergence theorem for seminormed resp semiconormed fuzzy integrals is showed provided that the fuzzy measure is null additive resp pseudo null additive