a preconditioner for the steady state navier stokes equations we present a new method for solving the sparse linear system of equations arising from the discretization of the linearized steady state navier stokes equations also known as the oseen equations the solver is an iterative method of krylov subspace type for which we devise a preconditioner through a heuristic argument based on the fundamental solution tensor for the oseen operator the preconditioner may also be conceived through a weaker heuristic argument involving differential operators computations indicate that convergence for the preconditioned discrete oseen problem is only mildly dependent on the viscosity inverse reynolds number and most importantly that the number of iterations does not grow as the mesh size is reduced indeed since the preconditioner is motivated through analysis of continuous operators the number of iterations decreases for smaller mesh size which accords with better approximation of these operators

adaptive fuzzy neural network control for a dsp based permanent magnet linear synchronous motor servo drive an adaptive fuzzy neural network afnn control system is proposed to control the position of the mover of a field oriented control permanent magnet linear synchronous motor pmlsm servo drive system to track periodic reference trajectories in this paper in the proposed afnn control system an fnn with accurate approximation capability is employed to approximate the unknown dynamics of the pmlsm and a robust compensator is proposed to confront the inevitable approximation errors due to finite number of membership functions and disturbances including the friction force the adaptive learning algorithm that can learn the parameters of the fnn on line is derived using lyapunov stability theorem moreover to relax the requirement for the value of lumped uncertainty in the robust compensator which comprises a minimum approximation error optimal parameter vectors higher order terms in taylor series and friction force an adaptive lumped uncertainty estimation law is investigated furthermore all the control algorithms are implemented in a tms320c32 dsp based control computer the simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties

recognizing the tractability in big data computing due to the limitation on computational power of existing computers the polynomial time does not works for identifying the tractable problems in big data computing this paper adopts the sublinear time as the new tractable standard to recognize the tractability in big data computing and the random access turing machine is used as the computational model to characterize the problems that are tractable on big data first two pure tractable classes are first proposed one is the class mathrm pl consisting of the problems that can be solved in polylogarithmic time by a ratm the another one is the class mathrm st including all the problems that can be solved in sublinear time by a ratm the structure of the two pure tractable classes is deeply investigated and they are proved mathrm pl i subsetneq mathrm pl i 1 and mathrm pl subsetneq mathrm st then two pseudo tractable classes mathrm ptr and mathrm pte are proposed mathrm ptr consists of all the problems that can solved by a ratm in sublinear time after a ptime preprocessing by reducing the size of input dataset mathrm pte includes all the problems that can solved by a ratm in sublinear time after a ptime preprocessing by extending the size of input dataset the relations among the two pseudo tractable classes and other complexity classes are investigated and they are proved that mathrm pt subseteq mathrm p sqcap mathrm t 0 q subsetneq mathrm ptr 0 q and mathrm pt p mathrm p

inferring operating rules for reservoir operations using fuzzy regression and anfis the methods of ordinary least squares regression olsr fuzzy regression fr and adaptive network based fuzzy inference system anfis are compared in inferring operating rules for a reservoir operations optimization problem dynamic programming dp is used as an example optimization tool to provide the input output data set to be used by olsr fr and anfis models the coefficients of an fr model are found by solving a linear programming lp problem the objective function of the lp is to minimize the total fuzziness of the fr model which is related to the width of fuzzy coefficients in the regression model before applying fr to the reservoir operations problem two fr formulations and interval regression ir are first examined in a simple tutorial example anfis is also used to derive the reservoir operating rules as fuzzy if then rules the olsr fr and anfis based rules are then simulated and compared based on their performance in simulation the methods are applied to a long term planning problem as well as to a medium term implicit stochastic optimization model the results indicate that fr is useful to derive operating rules for a long term planning model where imperfect and partial information is available anfis is beneficial in medium term implicit stochastic optimization as it is able to extract important features of the system from the generated input output set and represent those features as general operating rules

on inexact preconditioners for nonsymmetric matrices inexact versions of the block triangular preconditioners for nonsymmetric matrices of block two by two structures in m f murphy g h golub and a j wathen siam j sci comput 21 2000 pp 1969 1972 and i c f ipsen siam j sci comput 23 2001 pp 1050 1051 are presented and the two preconditioners for symmetric block two by two matrices in c durazzi and v ruggiero numer linear algebra appl 10 2003 pp 673 688 are extended to general nonsymmetric matrices moreover we precisely describe the spectral properties of the preconditioned matrices and the finite step termination properties of the preconditioned krylov subspace iteration methods with an optimal or galerkin property with respect to these preconditioners several numerical examples are performed to illustrate the effectiveness of the proposed preconditioners

a sequential learning scheme for function approximation using minimal radial basis function neural networks this article presents a sequential learning algorithm for function approximation and time series prediction using a minimal radial basis function neural network rbfnn the algorithm combines the growth criterion of the resource allocating network ran of platt 1991 with a pruning strategy based on the relative contribution of each hidden unit to the overall network output the resulting network leads toward a minimal topology for the rbfnn the performance of the algorithm is compared with ran and the enhanced ran algorithm of kadirkamanathan and niranjan 1993 for the following benchmark problems 1 hearta from the benchmark problems database proben1 2 hermite polynomial and 3 mackey glass chaotic time series for these problems the proposed algorithm is shown to realize rbfnns with far fewer hidden neurons with better or same accuracy

an augmented lagrangian based approach to the oseen problem we describe an effective solver for the discrete oseen problem based on an augmented lagrangian formulation of the corresponding saddle point system the proposed method is a block triangular preconditioner used with a krylov subspace iteration like bicgstab the crucial ingredient is a novel multigrid approach for the 1 1 block which extends a technique introduced by schoberl for elasticity problems to nonsymmetric problems our analysis indicates that this approach results in fast convergence independent of the mesh size and largely insensitive to the viscosity we present experimental evidence for both isop2 p0 and isop2 p1 finite elements in support of our conclusions we also show results of a comparison with two state of the art preconditioners showing the competitiveness of our approach

a relaxed dimensional factorization preconditioner for the incompressible navier stokes equations in this paper we introduce a relaxed dimensional factorization rdf preconditioner for saddle point problems properties of the preconditioned matrix are analyzed and compared with those of the closely related dimensional splitting ds preconditioner recently introduced by benzi and guo 7 numerical results for a variety of finite element discretizations of both steady and unsteady incompressible flow problems indicate very good behavior of the rdf preconditioner with respect to both mesh size and viscosity

preconditioned hermitian and skew hermitian splitting methods for non hermitian positive semidefinite linear systems for the positive semidefinite system of linear equations of a block two by two structure by making use of the hermitian skew hermitian splitting iteration technique we establish a class of preconditioned hermitian skew hermitian splitting iteration methods theoretical analysis shows that the new method converges unconditionally to the unique solution of the linear system moreover the optimal choice of the involved iteration parameter and the corresponding asymptotic convergence rate are computed exactly numerical examples further confirm the correctness of the theory and the effectiveness of the method

eigenvalue estimates for saddle point matrices of hermitian and indefinite leading blocks we study the eigenvalue bounds for the nonsingular saddle point matrices of hermitian and indefinite 1 1 and 2 2 blocks without imposing the restrictions that the 1 1 blocks are positive definite on the kernels of the 2 1 blocks

extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and kalman filter abstract this paper proposes a hybrid algorithm for extracting important fuzzy rules from a given rule base to construct a parsimonious fuzzy model with a high generalization ability this algorithm combines the advantages of genetic algorithms strong search capacity and kalman filter s fast convergence merit each random combination of the rules in the rule base is coded into a binary string and treated as a chromosome in genetic algorithms the binary string indicates the structure of a fuzzy model the parameters of the model are then estimated using the kalman filter in order to achieve a trade off between the accuracy and the complexity of a fuzzy model the schwarz rissanen criterion is used as an evaluation function in the hybrid algorithm the practical applicability of the proposed algorithm is examined by computer simulations on a human operator modeling problem and a nonlinear system modeling problem

efficient preconditioning of the linearized navier stokes equations for incompressible flow we outline a new class of robust and efficient methods for solving subproblems that arise in the linearization and operator splitting of navier stokes equations we describe a very general strategy for preconditioning that has two basic building blocks a multigrid v cycle for the scalar convection diffusion operator and a multigrid v cycle for a pressure poisson operator we present numerical experiments illustrating that a simple implementation of our approach leads to an effective and robust solver strategy in that the convergence rate is independent of the grid robust with respect to the time step and only deteriorates very slowly as the reynolds number is increased

block preconditioners based on approximate commutators this paper introduces a strategy for automatically generating a block preconditioner for solving the incompressible navier stokes equations we consider the pressure convection diffusion preconditioners proposed by kay loghin and wathen siam j sci comput 24 2002 pp 237 256 and silvester elman kay and wathen j comput appl math 128 2001 pp 261 279 numerous theoretical and numerical studies have demonstrated mesh independent convergence on several problems and the overall efficacy of this methodology a drawback however is that it requires the construction of a convection diffusion operator denoted f p projected onto the discrete pressure space this means that integration of this idea into a code that models incompressible flow requires a sophisticated understanding of the discretization and other implementation issues something often held only by the developers of the model as an alternative we consider automatic ways of computing f p based on purely algebraic considerations the new methods are closely related to the bfbt preconditioner of elman siam j sci comput 20 1999 pp 1299 1316 we use the fact that the preconditioner is derived from considerations of commutativity between the gradient and convection diffusion operators together with methods for computing sparse approximate inverses to generate the required matrix f p automatically we demonstrate that with this strategy the favorable convergence properties of the preconditioning methodology are retained

a modified dimensional split preconditioner for generalized saddle point problems abstract for large and sparse saddle point linear systems arising from 2d linearized navier stokes equations benzi and guo recently studied a dimensional split ds preconditioner appl numer math 61 2011 66 76 by further applying it to generalized saddle point problems in this paper we present a modified dimensional split mds preconditioner this new preconditioner is based on a splitting of the generalized saddle point matrix resulting in an unconditional convergent fixed point iteration the basic iteration is accelerated by a krylov subspace method like restarted gmres the implementation of the mds preconditioner is discussed and a similar case is also analyzed finally numerical experiments of a model navier stokes problem are presented to illustrate the effectiveness of the mds preconditioner

an approach for on line extraction of fuzzy rules using a self organising fuzzy neural network this paper presents a hybrid neural network called the self organising fuzzy neural network sofnn to extract fuzzy rules from the training data the first hidden layer of this network consists of ellipsoidal basis function ebf neurons every ebf neuron in the sofnn has both a centre vector and a width vector neurons are organised by the network itself the methods of the structure and parameter learning based on new adding and pruning techniques and a recursive learning algorithm are simple and effective with a high accuracy and a compact structure simulations show that the sofnn has the capability to encode fuzzy rules in the resulting network

least squares preconditioners for stabilized discretizations of the navier stokes equations this paper introduces two stabilization schemes for the least squares commutator lsc preconditioner developed by elman howle shadid shuttleworth and tuminaro siam j sci comput 27 2006 pp 1651 1668 for the incompressible navier stokes equations this preconditioning methodology is one of several choices that are effective for navier stokes equations and it has the advantage of being defined from strictly algebraic considerations it has previously been limited in its applicability to div stable discretizations of the navier stokes equations this paper shows how to extend the same methodology to stabilized low order mixed finite element approximation methods

updates of relational views the problem of translating updates of database views is studied view updates are disambi guated by requiring that a specified view complement i e a second view that contains all the information omitted from the given view remain constant during the translation some of the computational problems related to the apphcafion of this general methodology in the context of relational databases are studied projective views of databases that consist of a single relation and satisfy funcuonal dependencies are emphasized after characterizing complementary views the authors show that finding a minimum complement of a given view is np complete the problem of translating the insertion of a tuple into a view is then studied in detail and the results are extended to the cases of deletion and replacement of a tuple finally the explicit functional dependencies a new kind of dependency that intuitively states that some part of the database information can be computed from the rest are defined and studied

an evolving construction scheme for fuzzy systems this paper proposes an evolving construction scheme for fuzzy systems ecsfs ecsfs begins with a simple fuzzy system and evolves its structure by adding more fuzzy terms and rules to achieve a better accuracy in a greedy way an interesting feature of ecsfs is that it is able to automatically locate mathematically meaningful points such as the extremum and inflexion points of the approximated function one by one and then adds fuzzy terms based on these points fuzzy systems with such extreme points like their fuzzy terms are more efficient than other fuzzy systems by using the same number of fuzzy rules as a result ecsfs often achieves a better accuracy for fuzzy system identification compared with the previous methods when using the same number of fuzzy rules a number of simulation results are given to illustrate the advantages of the proposed scheme

on the complexity of sampling query feedback restricted database repair of functional dependency violations an inconsistent database is a database instance violating integrity constraints a repair of an inconsistent database is a maximal consistent subset sampling from the repair space is an alternative approach meeting the needs of many applications in this paper we introduce a new class of repair query feedback restricted repair based on the feedback on user s witness query we first map out a picture of both data and combined complexities of repair existence problems under different cases to identify the intractable cases especially we show that if the query is a projection or a union query then the decision problem is np complete even worse if the query is a conjunctive query the decision problem becomes 2 p complete however we prove that the combined complexity of the repair existence problem is in logspace when the witness query is a selection join query and this conclusion also implies that the combined complexity of side effect free deletion propagation problem under group deletion is in logspace which is not considered in previous works additionally we provide a polynomial random repair sampling algorithm under combined complexity at last we revisit the key preserving condition 1 and show that it will simplify the problem i e some cases become tractable for certain key preserving views as opposed to their counterparts that are not key preserving

efficient fuzzy partition of pattern space for classification problems abstract this paper proposes an efficient fuzzy partition method of a pattern space for classification problems the proposed method is based on the sequential subdivision of fuzzy subspaces and the generated fuzzy subspaces have different sizes in the proposed method first an n dimensional pattern space is divided into 2 n fuzzy subspaces with the same size next one of the fuzzy subspaces is selected and subdivided into 2 n fuzzy subspaces this procedure is iterated until a stopping condition is satisfied some criteria for selecting a fuzzy subspace to be subdivided are proposed and compared with each other by computer simulations the proposed method is also compared with other fuzzy classification methods

fuzzy basis functions universal approximation and orthogonal least squares learning fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions using the stone weierstrass theorem it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy based on the fuzzy basis function representations an orthogonal least squares ols learning algorithm is developed for designing fuzzy systems based on given input output pairs then the ols algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system the fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system and the simulation results show that the control performance is improved by incorporating some common sense fuzzy control rules

a variant of relaxed triangular splitting preconditioners for generalized saddle point problems from navier stokes equations abstract based on the dimensional split preconditioner in this paper a variant of relaxed triangular splitting preconditioner vrts is presented and discussed in which a simple and feasible way is designed for the selection of the parameters spectral properties of the vrts preconditioned matrix are analyzed in detail theoretical analysis shows that when applying the vrts preconditioner within a krylov subspace method less computational cost is required at each iteration than some state of the art approaches finally some numerical experiments arising from the discretization of navier stokes equations are given to illustrate and validate the efficiency and robustness of the presented preconditioners with using gmres as an iterative solver

structured preconditioners for nonsingular matrices of block two by two structures for the large sparse block two by two real nonsingular matrices we establish a general framework of practical and efficient structured preconditioners through matrix transformation and matrix approximations for the specific versions such as modified block jacobi type modified block gauss seidel type and modified block unsymmetric symmetric gauss seidel type preconditioners we precisely describe their concrete expressions and deliberately analyze eigenvalue distributions and positive definiteness of the preconditioned matrices also we show that when these structured preconditioners are employed to precondition the krylov subspace methods such as gmres and restarted gmres fast and effective iteration solvers can be obtained for the large sparse systems of linear equations with block two by two coefficient matrices in particular these structured preconditioners can lead to efficient and high quality preconditioning matrices for some typical matrices from the real world applications

sef view deletion under bounded condition abstract deletion propagation problem has been an important role in data lineage is a classic problem in the database research on data provenance and quality as a more restricted version of classic view update problem in relational databases it is defined on a given database d a monotonic relational algebraic query q and its materialized view v of d deletion propagation aims to find a tuple set d whose deletion from d guarantees that the result of q is exactly the view v minus an intended deletion v i e d will not produce side effect on view such d is side effect free if no side effect produced by deleting it from d we study the complexity of finding side effect free deletion in this paper we also consider the functional dependency restricted version deletion propagation problem b dp which generalizes the computational issues involved in data lineage it is to find the d not only view side effect free but also without introducing inconsistency with respect to the predefined functional dependencies both data and combined complexity of b dp are studied under both single and group deletion plenty of analysis are provided on this problem with respect to queries in different classes on either complexity aspect

new preconditioners for saddle point problems we present new preconditioners based on matrix splittings for the saddle point problems the spectral property of one of the preconditioned matrix is studied in detail numerical examples are used to illustrate the efficiency of the new preconditioners

parameter estimates for the relaxed dimensional factorization preconditioner and application to hemodynamics we present new results on the relaxed dimensional factorization rdf preconditioner for solving saddle point problems from incompressible flow simulations first introduced in benzi et al 2011 this method contains a parameter 0 0 to be chosen by the user previous works provided an estimate of in the 2d case using local fourier analysis a novel algebraic estimation technique for finding a suitable value of the rdf parameter in both the 2d and the 3d case with arbitrary geometries is proposed this technique is tested on a variety of discrete saddle point problems arising from the approximation of the navier stokes equations using a marker and cell scheme and a finite element one we also show results for a large scale problem relevant for hemodynamics simulation that we solve in parallel using up to 8196 cores

adaptive neuro fuzzy inference systems based approach to nonlinear noise cancellation for images the adaptive neuro fuzzy inference system anfis is an effective tool that can be applied to induct rules from observations e g pattern recognition in this paper we extend the nonlinear noise cancellation method using anfis from 1 d signals to 2 d counterpart images first the image restoration contaminated with gaussian noise is investigated in nonlinear passage dynamics of order 2 we inspect eight types of membership functions mf bell mf triangle mf gaussian mf two sided gaussian mf pi shaped mf product of two sigmoidal mfs difference of two sigmoidal mfs and trapezoidal mf in addition the other parameters such as the training epochs the number of mfs for each input the optimization method the type of output mfs and the over fitting problem are investigated for comparison with the noise cancellation using anfis we simulate 22 conventional filtering techniques spatial filters optimal wiener filter frequency domain filters wavelet wavelet packet 2 d adaptive filters etc the quality in terms of mean square error mse of image restoration using the proposed noise cancellation using anfis pi shaped mf is at least 75 times better for gaussian noise than that derived using any of these conventional filtering techniques

spectral properties of a class of matrix splitting preconditioners for saddle point problems based on the accelerated hermitian and skew hermitian splitting iteration scheme bai and golub 2007 we propose a new two parameter matrix splitting preconditioner in this paper spectral properties of the preconditioned matrix are analyzed in detail furthermore based on this preconditioner an improved version of matrix splitting preconditioner is presented and analyzed finally performance of the preconditioners is compared by using gmres m as an iterative solver on linear systems arising from the discretization of stokes and navier stokes equations