efficient analysis of em scattering from bodies of revolution via the aca in this communication the adaptive cross approximation aca algorithm is utilized to analyze the scattering from arbitrary metallic bodies of revolution bors that is formulated by the electric field integral equation approach for a given mode a multilevel partitioning is used to group the basis functions of the bors along the longitudinal dimension the interactions of the adjacent groups are calculated directly by the method of moments mom and the interactions of well separated groups are compressed by the aca algorithm the memory requirement and cpu time consumption are reduced drastically

a pseudospectral method for time domain computation of electromagnetic scattering by bodies of revolution we present a multidomain pseudospectral method for the accurate and efficient time domain computation of scattering by body of revolution bor perfectly electrically conducting objects in the bor formulation of the maxwell equations the azimuthal dependence of the fields is accounted for analytically through a fourier series the numerical scheme in the r z plane is developed in general curvilinear coordinates and the method of characteristics is applied for patching field values in the individual subdomains to obtain the global solution a modified matched layer method is used for terminating the computational domain and special attention is given to proper treatment of the coordinate singularity in the scattered field formulation and correct time domain boundary conditions along edges numerical results for monochromatic plane wave scattering by smooth and nonsmooth axis symmetric objects including spheres cone spheres and finite cylinders is compared with results from the literature illustrating the accuracy and computational efficiency associated with the use of properly constructed spectral methods to emphasize the versatility of the presented framework we compute plane wave scattering by a missile and find satisfactory agreement with method of moment mom computations

state estimation for delayed neural networks in this letter the state estimation problem is studied for neural networks with time varying delays the interconnection matrix and the activation functions are assumed to be norm bounded the problem addressed is to estimate the neuron states through available output measurements such that for all admissible time delays the dynamics of the estimation error is globally exponentially stable an effective linear matrix inequality approach is developed to solve the neuron state estimation problem in particular we derive the conditions for the existence of the desired estimators for the delayed neural networks we also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities lmis finally it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks numerical examples are included to illustrate the applicability of the proposed design method

plane wave analysis and comparison of split field biaxial and uniaxial pml methods as abcs for pseudospectral electromagnetic wave simulations in curvilinear coordinates in this paper we discuss and compare split field biaxial and uniaxial perfectly matched layer pml methods for absorbing outgoing vector waves in cylindrical and spherical coordinates we first extend berenger s split field formulation into spherical and cylindrical coordinates in such a way that it maintains all the desirable properties it exhibits in rectangular coordinates then we discuss the biaxial and the uniaxial medium pml methods in cartesian coordinates and extend them to spherical and cylindrical coordinates properties of plane wave solutions of the pml methods are analyzed in particular the decay and boundness properties of the solutions are considered in order to provide further insight into the different formulations presented herein moreover we propose a set of symmetric hyperbolic equations for both the biaxial and the uniaxial pml methods in the time domain which is fine tuned in numerical experiments and very suitable for time domain problems all three types of spherical and cylindrical pml methods are applied in simulations of plane wave scattering as well as radiating dipole problems we use a multidomain pseudospectral chebyshev numerical scheme and the effectiveness of the pml methods is demonstrated through the accurate numerical results obtained the order of outer boundary reflection is as low as 0 1 of the exact solution

batalin vilkovisky formalism in perturbative algebraic quantum field theory on the basis of a thorough discussion of the batalin vilkovisky formalism for classical field theory presented in our previous publication we construct in this paper the batalin vilkovisky complex in perturbatively renormalized quantum field theory the crucial technical ingredient is an extended notion of the renormalized time ordered product as a binary product equivalent to the pointwise product of classical field theory originally in causal perturbation theory the time ordered product is understood merely as a sequence of multilinear maps on the space of local functionals our extended notion of the renormalized time ordered product denoted by cdot mathcal t rm r is consistent with the old one and we found a subspace of the quantum algebra which is closed with respect to cdot mathcal t rm r on this space the renormalized batalin vilkovisky algebra is then the classical algebra but written in terms of the time ordered product together with an operator which replaces the ill defined graded laplacian of the unrenormalized theory we identify it with the anomaly term of the anomalous master ward identity of brennecke and dutsch contrary to other approaches we do not refer to the path integral formalism and do not need to use regularizations in intermediate steps

on the azimuthal fourier components of the green s function for the helmholtz equation in three dimensions many algorithms that compute acoustic or electromagnetic fields scattered by surfaces of revolution require fast evaluation of the azimuthal fourier components gm of the green s function for the helmholtz equation in three dimensions in this article we derive a recurrence relation for the functions gm and obtain explicit formulas for their partial derivatives these observations significantly reduce the complexity of the computation of the scattered fields generated by axisymmetric scatterers

on designing observers for time delay systems with non linear disturbances in this paper the observer design problem is studied for a class of time delay non linear systems the system under consideration is subject to delayed state and non linear disturbances the time delay is allowed to be time varying and the non linearities are assumed to satisfy global lipschitz conditions the problem addressed is the design of state observers such that for the admissible time delay as well as non linear disturbances the dynamics of the observation error is globally exponentially stable an effective algebraic matrix inequality approach is developed to solve the non linear observer design problem specifically some conditions for the existence of the desired observers are derived and an explicit expression of desired observers is given in terms of some free parameters a simulation example is included to illustrate the practical applicability of the proposed theory

h sub infinity control and quadratic stabilization of systems with parameter uncertainty via output feedback the article concerns linear systems which are subject to both time varying norm bounded parameter uncertainty and exogenous disturbance it addresses the robust h sub infinity control problem of designing a linear dynamic output feedback controller such that the closed loop system is quadratically stable and achieves a prescribed level of disturbance attenuation for all admissible parameter uncertainties it is shown that such a problem is equivalent to a scaled h sub infinity control problem

radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems this paper presents a means to approximate the dynamic and static equations of stochastic nonlinear systems and to estimate state variables based on radial basis function neural network rbfnn after a nonparametric approximate model of the system is constructed from a priori experiments or simulations a suboptimal filter is designed based on the upper bound error in approximating the original unknown plant with nonlinear state and output equations the procedures for both training and state estimation are described along with discussions on approximation error nonlinear systems with linear output equations are considered as a special case of the general formulation finally applications of the proposed rbfnn to the state estimation of highly nonlinear systems are presented to demonstrate the performance and effectiveness of the method

accurate and efficient computation of the modal green s function arising in the electric field integral equation for a body of revolution we present a computationally efficient way to compute the modal green s function arising in the electric field integral equation for a body of revolution the computation of this function is time consuming due to its singular and oscillating nature especially for high fourier mode indices efficient and accurate computation of this function is important to arrive at a fast numerical method for analyzing the scattering problem of a body of revolution we compute the modal green s function up to machine precision in a well controlled way with limited effort even for large bodies

modal characteristic basis function method for solving scattering from multiple conducting bodies of revolution the analysis of scattering from multiple bodies of revolution bors has important applications in microwave remote sensing radar imaging and other areas for single bor three dimensional problem can be degenerated into 2 5 dimensional problem based on the modal green functions mgfs reducing the solving time and storage remarkably however it is difficult to extend the mgf to multiple arbitrarily orientated bors this paper presents a modal characteristic basis function method for solving the scattering from multiple bors it combines the mgf of bor with the characteristic basis function method named as the bor cbf method here the mgf of bor is first applied to efficiently solve the current distribution on the surface of each bor the singular value decomposition is then used to construct the global modal characteristic basis functions mcbfs for each bor finally the mcbfs are transformed into the rwg basis functions using a basis function mapping technique so that the filling of the interaction matrix can be accelerated by the multilevel fast multipole algorithm by using the bor cbf method the scattering of multiple arbitrarily orientated bors can be solved efficiently numerical results are presented to demonstrate the accuracy and efficiency of the bor cbf method

a spectral integral method for smooth multilayered bodies of revolution a spectral integral method sim is developed to solve the scattering problem from smooth multilayered bodies of revolution bors this sim is a spectral method to improve the accuracy and efficiency of the sim by the fast fourier transform fft algorithm this method can achieve high accuracy and greatly reduce the computational time consumption in our bor sim the bor generatrix current is expanded by the truncated fourier series then the impedance matrix is generated in the spectral domain to solve the scattering problem unlike the conventional methods that need eight to ten segments per wavelength in the bor sim two points per wavelength are adequate to achieve high accuracy according to the nyquist theorem being accelerated by fft this method shows a significant efficiency advantage over the conventional bor method numerical results demonstrate the accuracy and efficiency of our new method by combining with the poggio miller chang harrington wu tsai surface integral equations this method can be used to solve many practical scattering problems

the generally covariant locality principle a new paradigm for local quantum field theory a new approach to the model independent description of quantum field theories will be introduced in the present work the main feature of this new approach is to incorporate in a local sense the principle of general covariance of general relativity thus giving rise to the concept of a locally covariant quantum field theory such locally covariant quantum field theories will be described mathematically in terms of covariant functors between the categories on one side of globally hyperbolic spacetimes with isometric embeddings as morphisms and on the other side of algebras with unital injective monomorphisms as morphisms moreover locally covariant quantum fields can be described in this framework as natural transformations between certain functors the usual haag kastler framework of nets of operator algebras over a fixed spacetime background manifold together with covariant automorphic actions of the isometry group of the background spacetime can be re gained from this new approach as a special case examples of this new approach are also outlined in case that a locally covariant quantum field theory obeys the time slice axiom one can naturally associate to it certain automorphic actions called relative cauchy evolutions which describe the dynamical reaction of the quantum field theory to a local change of spacetime background metrics the functional derivative of a relative cauchy evolution with respect to the spacetime metric is found to be a divergence free quantity which has as will be demonstrated in an example the significance of an energy momentum tensor up to addition of scalar functions for the locally covariant quantum field theory furthermore we discuss the functorial properties of state spaces of locally covariant quantum field theories that entail the validity of the principle of local definiteness

observer design for switched recurrent neural networks an average dwell time approach this paper is concerned with the problem of observer design for switched recurrent neural networks with time varying delay the attention is focused on designing the full order observers that guarantee the global exponential stability of the error dynamic system based on the average dwell time approach and the free weighting matrix technique delay dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities the error state decay estimate is also given then the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results finally four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods

quantum field theories on categories fibered in groupoids we introduce an abstract concept of quantum field theory on categories fibered in groupoids over the category of spacetimes this provides us with a general and flexible framework to study quantum field theories defined on spacetimes with extra geometric structures such as bundles connections and spin structures using right kan extensions we can assign to any such theory an ordinary quantum field theory defined on the category of spacetimes and we shall clarify under which conditions it satisfies the axioms of locally covariant quantum field theory the same constructions can be performed in a homotopy theoretic framework by using homotopy right kan extensions which allows us to obtain first toy models of homotopical quantum field theories resembling some aspects of gauge theories

new criteria for global robust stability of delayed neural networks with norm bounded uncertainties in this paper we study the global asymptotic robust stability of delayed neural networks with norm bounded uncertainties by employing the lyapunov stability theory and homeomorphic mapping theorem we derive some new types of sufficient conditions ensuring the existence uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slope bounded activation functions an important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties of symmetric matrices associated with the uncertainty sets of the network parameters the obtained results are shown to be generalizations of some of the previously published corresponding results some comparative numerical examples are also constructed to compare our results with some closely related existing literature results

passivity of switched recurrent neural networks with time varying delays this paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time varying delays first using the multiple lyapunov functions method a state dependent switching law is designed to present a stochastic passivity condition second a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state dependent switching third based on the average dwell time approach a class of switching signals is determined to guarantee the switched neural network stochastically passive finally three numerical examples are provided to illustrate the characteristics of three proposed switching laws

full wave analysis and optimization of a tara like shield assisted paraboloidal reflector antenna using a nystrom type method using the recently developed method based on the rigorous theory of singular and hypersingular integral equations ies and nystrom type discretization a paraboloidal reflector assisted with a conical shield is investigated to decrease the computation time we propose a new method of calculation of the modal green s functions in the quasi optical range the far and near fields of the studied reflector are analyzed in the transmission and reception cases and reveal fine features that escape asymptotic analysis elementary numerical optimization of the shield assisted paraboloidal antenna is performed

design of state estimator for bam fuzzy cellular neural networks with leakage and unbounded distributed delays in this paper the state estimation problem is studied for the bidirectional associative memory bam fuzzy cellular neural networks fcnns with delays where the delays include leakage and unbounded distributed delays the problem addressed is to estimate the neuron states through available output measurements such that for all admissible leakage and unbounded distributed delays the dynamics of the estimation error is globally asymptotically stable a delay dependent linear matrix inequality lmi criterion for the existence of the estimator is proposed by using the lyapunovkrasovskii functional method in addition the unknown gain matrix is determined by solving a delay dependent lmi finally numerical examples and simulations are provided to illustrate the effectiveness of the theoretical results

robust filtering for a class of stochastic uncertain nonlinear time delay systems via exponential state estimation we investigate the robust filter design problem for a class of nonlinear time delay stochastic systems the system under study involves stochastics unknown state time delay parameter uncertainties and unknown nonlinear disturbances which are all often encountered in practice and the sources of instability the aim of this problem is to design a linear delayless uncertainty independent state estimator such that for all admissible uncertainties as well as nonlinear disturbances the dynamics of the estimation error is stochastically exponentially stable in the mean square independent of the time delay sufficient conditions are proposed to guarantee the existence of desired robust exponential filters which are derived in terms of the solutions to algebraic riccati inequalities the developed theory is illustrated by numerical simulation

model independent comparison between factorization algebras and algebraic quantum field theory on lorentzian manifolds this paper investigates the relationship between algebraic quantum field theories and factorization algebras on globally hyperbolic lorentzian manifolds functorial constructions that map between these two types of theories in both directions are developed under certain natural hypotheses including suitable variants of the local constancy and descent axioms the main result is an equivalence theorem between cauchy constant and additive algebraic quantum field theories and cauchy constant additive and time orderable prefactorization algebras a concept of involution for the latter class of prefactorization algebras is introduced via transfer this involves cauchy constancy explicitly and does not extend to generic time orderable prefactorization algebras

some novel approaches on state estimation of delayed neural networks this paper studies the issue of state estimation for a class of neural networks nns with time varying delay a novel lyapunov krasovskii functional lkf is constructed where triple integral terms are used and a secondary delay partition approach sdpa is employed compared with the existing delay partition approaches the proposed approach can exploit more information on the time delay intervals by taking full advantage of a modified wirtinger s integral inequality mwii improved delay dependent stability criteria are derived which guarantee the existence of desired state estimator for delayed neural networks dnns a better estimator gain matrix is obtained in terms of the solution of linear matrix inequalities lmis in addition a new activation function dividing method is developed by bringing in some adjustable parameters three numerical examples with simulations are presented to demonstrate the effectiveness and merits of the proposed methods

variance constrained state estimation for complex networks with randomly varying topologies this paper investigates the variance constrained h infty state estimation problem for a class of nonlinear time varying complex networks with randomly varying topologies stochastic inner coupling and measurement quantization a kronecker delta function and markovian jumping parameters are utilized to describe the random changes of network topologies a gaussian random variable is introduced to model the stochastic disturbances in the inner coupling of complex networks as a kind of incomplete measurements measurement quantization is taken into consideration so as to account for the signal distortion phenomenon in the transmission process stochastic nonlinearities with known statistical characteristics are utilized to describe the stochastic evolution of the complex networks we aim to design a finite horizon estimator such that in the simultaneous presence of quantized measurements and stochastic inner coupling the prescribed variance constraints on the estimation error and the desired h infty performance requirements are guaranteed over a finite horizon sufficient conditions are established by means of a series of recursive linear matrix inequalities and subsequently the estimator gain parameters are derived a simulation example is presented to illustrate the effectiveness and applicability of the proposed estimator design algorithm

stability analysis for stochastic cohen grossberg neural networks with mixed time delays in this letter the global asymptotic stability analysis problem is considered for a class of stochastic cohen grossberg neural networks with mixed time delays which consist of both the discrete and distributed time delays based on an lyapunov krasovskii functional and the stochastic stability analysis theory a linear matrix inequality lmi approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square it is shown that the addressed stochastic cohen grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two lmis are feasible where the feasibility of lmis can be readily checked by the matlab lmi toolbox it is also pointed out that the main results comprise some existing results as special cases a numerical example is given to demonstrate the usefulness of the proposed global stability criteria

global attractivity in delayed hopfield neural network models two different approaches are employed to investigate the global attractivity of delayed hopfield neural network models without assuming the monotonicity and differentiability of the activation functions liapunov functionals and functions combined with the razumikhin technique are constructed and employed to establish sufficient conditions for global asymptotic stability independent of the delays in the case of monotone and smooth activation functions the theory of monotone dynamical systems is applied to obtain criteria for global attractivity of the delayed model such criteria depend on the magnitude of delays and show that self inhibitory connections can contribute to the global convergence

robust filter design with time varying parameter uncertainty and error variance constraints in this paper we consider the design of robust filters for linear continuous and discrete time systems subjected to timevarying parameter uncertainties and estimation error variance constraints the problem addressed is the design of robust filters which do not depend on uncertainty such that for all admissible time varying parameter perturbations the variance of the estimation error of each state is guaranteed to be not more than the individual prespecified upper bound in both continuous and discrete time cases it is shown that the computation of the filter matrices calls for the solution of a pair of algebraic riccati equations specifically the existence conditions and the explicit expression of the desired filters are obtained furthermore two illustrative examples are presented to demonstrate the effectiveness of the proposed design procedure

sampled data state estimation of markovian jump static neural networks with interval time varying delays abstract in this paper we consider the problem of sampled data state estimation of markovian jump delayed static neural networks by constructing a suitable lyapunov krasovskii functional with double and triple integral terms and using jensen inequality delay dependent criteria are presented so that the error system is asymptotically stable instead of the continuous measurement the sampled measurement is employed to estimate the neuron states it is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities which is efficiently facilitated by available algorithms finally two numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results

equivalence principle algorithm with body of revolution equivalence surface for the modeling of large multiscale structures we propose a multiscale solver of equivalence principle algorithm with a body of revolution bor equivalence surface es first the whole object is decomposed into subdomains the ess are defined as proper bors e g spheres in this work to enclose each subdomain the rao wilton glisson rwg and bor basis functions are defined on each sphere respectively second the octree is constructed in each nonzero subdomain the multilevel fast multipole algorithm mlfma is employed to solve the equivalent currents on the es individually and then the equivalent currents are projected from rwg onto the bor basis functions the couplings between neighboring subdomains are evaluated directly using mlfma and the separated subdomains are substituted by the es to es couplings and are evaluated efficiently by the bor method of moments bor mom to solve the equation system a hybrid inner and outer iterative solver is employed where the inner iteration is used to solve each local subdomains and the outer iteration is used to update the global solutions by collecting all the local solutions numerical results and discussions demonstrate the validity of the proposed work

delay dependent state estimation for delayed neural networks in this letter the delay dependent state estimation problem for neural networks with time varying delay is investigated a delay dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable the proposed method is based on the free weighting matrix approach and is applicable to the case that the derivative of a time varying delay takes any value an algorithm is presented to compute the state estimator finally a numerical example is given to demonstrate the effectiveness of this approach and the improvement over existing ones

global exponential stability and global convergence in finite time of delayed neural networks with infinite gain this paper introduces a general class of neural networks with arbitrary constant delays in the neuron interconnections and neuron activations belonging to the set of discontinuous monotone increasing and possibly unbounded functions the discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high and tends to infinity while the delay accounts for the finite switching speed of the neuron amplifiers or the finite signal propagation speed it is known that the delay in combination with high gain nonlinearities is a particularly harmful source of potential instability the goal of this paper is to single out a subclass of the considered discontinuous neural networks for which stability is instead insensitive to the presence of a delay more precisely conditions are given under which there is a unique equilibrium point of the neural network which is globally exponentially stable for the states with a known convergence rate the conditions are easily testable and independent of the delay moreover global convergence in finite time of the state and output is investigated in doing so new interesting dynamical phenomena are highlighted with respect to the case without delay which make the study of convergence in finite time significantly more difficult the obtained results extend previous work on global stability of delayed neural networks with lipschitz continuous neuron activations and neural networks with discontinuous neuron activations but without delays

closed form modal green s functions for accelerated computation of bodies of revolution to solve problems of bodies of revolution bor using the method of moments the modal green s function is used which is defined as the radiation field of a circular loop antenna with sinusoidal current distribution it is a group of oscillating integrals that forms the bottleneck for further accelerating the computation of bor problems in this paper we propose closed form expressions for near axis far distance modal green s functions which can be used to accelerate the computation of bor problems especially when the object is slim a criterion is presented based on a rigorous error analysis to guarantee its range of applications both the scattering and radiation examples are presented to demonstrate the validity and efficiency of the proposed expressions

h_ infty state estimation for complex networks with uncertain inner coupling and incomplete measurements in this paper the h state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements with the aid of the interval matrix approach we make the first attempt to characterize the uncertainties entering into the inner coupling matrix the incomplete measurements under consideration include sensor saturations quantization and missing measurements all of which are assumed to occur randomly by introducing a stochastic kronecker delta function these incomplete measurements are described in a unified way and a novel measurement model is proposed to account for these phenomena occurring with individual probability with the measurement model a set of h state estimators is designed such that for all admissible incomplete measurements as well as the uncertain coupling strength the estimation error dynamics is exponentially mean square stable and the h performance requirement is satisfied the characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem that can be easily solved using the semidefinite program method finally a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach

the sound field of a rotor in a stationary duct a method is developed for the prediction of noise from rotors in an axisymmetric duct at rest the technique combines a previously developed model for noise from disc sources with a boundary integral equation method for scattering from the duct surface recent developments in the theory of gaussian quadrature and generalized elliptic integrals are used to simplify the implementation of the formulation the sound field of a simplified model ducted rotor is then calculated and its structure examined in the framework of known features of the field around rotating sources it is found that as for an open rotor the sonic radius plays an important role in the structure of the field

minimum entropy filtering for multivariate stochastic systems with non gaussian noises in this note a minimum entropy filtering algorithm is presented for a class of multivariate dynamic stochastic systems which are represented by a set of time varying difference equations and are subjected to the multivariate non gaussian stochastic inputs several new concepts including the hybrid random vector hybrid probability and hybrid entropy are firstly established to describe the probabilistic property of the estimation errors new relationships are provided between the probability density functions pdfs of the multivariate stochastic input and output for different mapping cases recursive algorithms are then proposed to design the real time sub optimal filter so that the hybrid entropy of the estimation error can be minimized finally an improved algorithm is provided through the on line tuning of the weighting matrices so as to guarantee the local stability of the error system

combining an extrapolation technique with the method of moments for solving large scattering problems involving bodies of revolution the purpose of this work is to combine an extrapolation technique with the method of moments mom to solve scattering problems involving large bodies it has been shown in a previous work that the current induced on the smooth parts of large scatterers may be represented as a series of complex exponential functions with a few terms based on this concept a hybrid set of basis functions is constructed using entire domain functions of complex exponential type on the smooth portion of the scatterer complemented by subdomain basis functions near edges and discontinuities an extrapolation procedure is developed in which the scattering problem is first solved for a portion of the scatterer using the conventional mom next a set of entire domain basis functions whose behaviour could be extrapolated with an increase in the size of the scatterer is extracted from this original solution the procedure outlined has the very desirable feature that the total number of basis functions remains unchanged even as the scatterer size is increased allowing for large scatterers to be handled with a relatively small number of unknowns the extrapolation technique is applied to scattering problems from bodies of revolution bors and numerical results for an open cylinder and a barrel shaped bor are presented

locally corrected nystrom method for em scattering by bodies of revolution the nystrom method allows for high order solutions to integral equations modifying the nystrom method with local corrections for singular integrands allows the method to be applied to the inherently singular kernels of frequency domain electromagnetic integral equations here the efficiency and high order convergence properties of the nystrom method are brought together with the body of revolution bor geometry to create an efficient approach to solving this important class of problems in this paper we summarize the locally corrected nystrom method and the magnetic field integral equation mfie for a bor scatterer results are presented that demonstrate high order convergence and excellent agreement with reference data

solution of pmchw integral equation for transient electromagnetic scattering from dielectric body of revolution the transient em scattering from a homogeneous dielectric body of revolution bor is formulated in terms of the time domain pmchw td pmchw integral equation triangular functions along the generatrix of the bor and weighted laguerre polynomials are used as the spatial and temporal basis functions respectively to expand the unknown equivalent surface electric magnetic current densities in this way both the memory requirement and the cpu time can be reduced largely since the matrix equation for each fourier mode can be solved independently numerical results are presented to demonstrate the feasibility of the proposed method moreover the convergence is discussed for both the weighted laguerre polynomials and the fourier mode

exponential stabilization of memristive neural networks with time delays in this paper a general class of memristive neural networks with time delays is formulated and studied some sufficient conditions in terms of linear matrix inequalities are obtained in order to achieve exponential stabilization the result can be applied to the closed loop control of memristive systems in particular several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks in addition a simplified and effective algorithm is considered for design of the optimal controller these conditions are the improvement and extension of the existing results in the literature two numerical examples are given to illustrate the theoretical results via computer simulations

the use of the fft for the efficient solution of the problem of electromagnetic scattering by a body of revolution the enhancement of the computational efficiency of the body of revolution scattering problem is discussed with a view of making it practical for solving large body problems the problem of the electromagnetic scattering by a perfectly conducting body is considered although the methods provided can be extended to multilayered dielectric bodies as well typically the generation of the elements of the moment method matrix consumes a major portion of the computational time it is shown how this time can be significantly reduced by manipulating the expression for the matrix elements in a manner that allows one to compute them efficiently by using the fast fourier transform fft a technique for extracting the singularity of the green s function that appears within the integrands of the matrix diagonal is also presented further enhancing the usefulness of the fft it is shown that with the use of the method discussed here the computational time can be improved by at least an order of magnitude for large bodies in comparison to that for previous algorithms

finite time state estimation for coupled markovian neural networks with sensor nonlinearities this paper investigates the issue of finite time state estimation for coupled markovian neural networks subject to sensor nonlinearities where the markov chain with partially unknown transition probabilities is considered a luenberger type state estimator is proposed based on incomplete measurements and the estimation error system is derived by using the kronecker product by using the lyapunov method sufficient conditions are established which guarantee that the estimation error system is stochastically finite time bounded and stochastically finite time stable respectively then the estimator gains are obtained via solving a set of coupled linear matrix inequalities finally a numerical example is given to illustrate the effectiveness of the proposed new design method

finite time state estimation for recurrent delayed neural networks with component based event triggering protocol this paper deals with the event based finite time state estimation problem for a class of discrete time stochastic neural networks with mixed discrete and distributed time delays in order to mitigate the burden of data communication a general component based event triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time point according to a specific triggering condition a new concept of finite time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling like time function the objective of the addressed problem is to construct an event based state estimator to estimate the neuron states such that in the presence of both mixed time delays and external noise disturbances the dynamics of the estimation error is finite time bounded in the mean square with a prescribed error upper bound sufficient conditions are established via stochastic analysis techniques to guarantee the desired estimation performance by solving an optimization problem with some inequality constraints the explicit expression of the estimator gain matrix is characterized to minimize the settling like time finally a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme

an explicit kernel split panel based nystrom scheme for integral equations on axially symmetric surfaces a high order accurate explicit kernel split panel based fourier nystrom dis cretization scheme is developed for integral equations associated with the helm holtz equation in axially symmetric domains extensive incorporation of ana lytic information about singular integral kernels and on the y computation of nearly singular quadrature rules allow for very high achievable accuracy also in the evaluation of elds close to the boundary of the computational domain

electromagnetic scattering for multiple pec bodies of revolution using equivalence principle algorithm an equivalence principle algorithm epa method is extended to analyze the electromagnetic scattering from multiple bodies of revolution mbor with the axes arbitrarily oriented equivalence spheres are used to enclose each bor and the equivalence currents are expanded by the basis functions of bodies of revolution bor to obtain the scattering operators and translation operators of epa for fourier modes independently the rotational symmetry systems are established in local bor coordinate systems the origin of the local bor coordinate system is located at the center of the equivalence sphere and the z axis coincides with the axis of the enclosed bor to obtain the scattering operator of each equivalence sphere whereas the origin is located at the observation sphere and z axis passes through the center of the source sphere to obtain the translation operator of each pair of equivalence spheres the current coefficient transformation algorithm is used to transform the equivalence currents among local bor coordinate systems the total equation is iteratively solved in the global coordinate system the proposed scheme is especially efficient for the analysis of scattering from mbor randomly distributed in electrically large scale region numerical results are given to demonstrate the efficiency

global stability analysis of neural networks with multiple time varying delays in this note we study the equilibrium and stability properties of neural networks with time varying delays our main results give sufficient conditions for the existence uniqueness and global asymptotic stability of the equilibrium point the proposed conditions establish the relationships between network parameters of the neural systems and the delay parameters the obtained results are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric some examples are also presented to compare our results with the previous results derived in the literature

robust state estimation for uncertain neural networks with time varying delay the robust state estimation problem for a class of uncertain neural networks with time varying delay is studied in this paper the parameter uncertainties are assumed to be norm bounded based on a new bounding technique a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks the criterion is dependent on the size of the time varying delay and on the size of the time derivative of the time varying delay it is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality lmi which can be easily facilitated by using some standard numerical packages finally two simulation examples are given to demonstrate the effectiveness of the developed approach

state estimation for discrete time dynamical networks with time varying delays and stochastic disturbances under the round robin protocol this paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time varying delays subject to the round robin protocol the communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network in which only one node is allowed to send data at each time instant the round robin protocol is utilized to orchestrate the transmission order of nodes by using a switch based approach the dynamics of the estimation error is modeled by a periodic parameter switching system with time varying delays the purpose of the problem addressed is to design an estimator such that the estimation error is exponentially ultimately bounded with a certain asymptotic upper bound in mean square subject to the process noise and exogenous disturbance furthermore such a bound is subsequently minimized by the designed estimator parameters a novel lyapunov like functional is employed to deal with the dynamics analysis issue of the estimation error sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square by applying the stochastic analysis approach then the desired estimator gains are characterized by solving a convex problem finally a numerical example is given to illustrate the effectiveness of the estimator design scheme

event triggered h_ infty state estimation for delayed stochastic memristive neural networks with missing measurements the discrete time case in this paper the event triggered h infty state estimation problem is investigated for a class of discrete time stochastic memristive neural networks dsmnns with time varying delays and missing measurements the dsmnn is subject to both the additive deterministic disturbances and the multiplicative stochastic noises the missing measurements are governed by a sequence of random variables obeying the bernoulli distribution for the purpose of energy saving an event triggered communication scheme is used for dsmnns to determine whether the measurement output is transmitted to the estimator or not the problem addressed is to design an event triggered h infty estimator such that the dynamics of the estimation error is exponentially mean square stable and the prespecified h infty disturbance rejection attenuation level is also guaranteed by utilizing a lyapunov krasovskii functional and stochastic analysis techniques sufficient conditions are derived to guarantee the existence of the desired estimator and then the estimator gains are characterized in terms of the solution to certain matrix inequalities finally a numerical example is used to demonstrate the usefulness of the proposed event triggered state estimation scheme

robust and reduced order filtering new lmi based characterizations and methods this paper addresses several challenging problems of robust filtering we derive new linear matrix inequality lmi characterizations of minimum variance or h sub 2 performance and demonstrate that they allow the use of parameter dependent lyapunov functions while preserving tractability of the problem the resulting conditions are less conservative than earlier techniques which are restricted to fixed not parameter dependent lyapunov functions the remainder of the paper discusses reduced order filter problems new lmi based nonconvex optimization formulations are introduced for the existence of reduced order filters and several efficient optimization algorithms of local and global optimization are proposed nontrivial and less conservative relaxation techniques are presented as well the viability and efficiency of the proposed approaches are then illustrated through computational experiments and comparisons with existing methods