the strict bounded real lemma in infinite dimensions abstract the strict bounded real lemma is generalized to an infinite dimensional setting this relates the existence of a stabilizing solution to a riccati equation to an h norm bound and to the existence of a solution to a riccati inequality

discriminant subspace analysis a fukunaga koontz approach the fisher linear discriminant fld is commonly used in pattern recognition it finds a linear subspace that maximally separates class patterns according to the fisher criterion several methods of computing the fld have been proposed in the literature most of which require the calculation of the so called scatter matrices in this paper we bring a fresh perspective to fld via the fukunaga koontz transform fkt we do this by decomposing the whole data space into four subspaces with different discriminabilities as measured by eigenvalue ratios by connecting the eigenvalue ratio with the generalized eigenvalue we show where the fisher criterion is maximally satisfied we prove the relationship between fld and fkt analytically and propose a unified framework to understanding some existing work furthermore we extend our theory to the multiple discriminant analysis mda this is done by transforming the data into intraclass and extraclass spaces followed by maximizing the bhattacharyya distance based on our fkt analysis we identify the discriminant subspaces of mda fkt and propose an efficient algorithm which works even when the scatter matrices are singular or too large to be formed our method is general and may be applied to different pattern recognition problems we validate our method by experimenting on synthetic and real data

feature extraction using constrained approximation and suppression in this paper we systematize a family of constrained quadratic classifiers that belong to the class of one class classifiers one class classifiers such as the single class support vector machine or the subspace methods are widely used for pattern classification and detection problems because they have many advantages over binary classifiers we interpret subspace methods as rank constrained quadratic classifiers in the framework we also introduce two constraints and a method of suppressing the effect of competing classes to make them more accurate and retain their advantages over binary classifiers experimental results demonstrate the advantages of our methods over conventional classifiers

polynomial modeling for time varying systems based on a particle swarm optimization algorithm in this paper an effective particle swarm optimization pso is proposed for polynomial models for time varying systems the basic operations of the proposed pso are similar to those of the classical pso except that elements of particles represent arithmetic operations and variables of time varying models the performance of the proposed pso is evaluated by polynomial modeling based on various sets of time invariant and time varying data results of polynomial modeling in time varying systems show that the proposed pso outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming gp and dynamic gp an analysis of the diversity of individuals of populations in the proposed pso and gp reveals why the proposed pso obtains better results than those obtained by gp

a delay decomposition approach to h admissibility for discrete time singular delay systems abstract this paper considers the problem of delay dependent admissibility condition and h performance analysis for discrete time singular delay systems by utilizing the delay decomposition approach an improved delay dependent admissibility condition is presented in terms of linear matrix inequalities lmis this admissibility condition is much less conservative than some existing results and includes those as its special cases based on the proposed condition a new delay dependent bounded real lemma is also given which guarantees the admissibility and the h performance numerical examples are given to illustrate the effectiveness of the proposed method

improved delay dependent bounded real lemma for uncertain time delay systems this paper concerns reducing the conservatism of delay dependent bounded real lemma brl of time delay systems with uncertainty described by a linear fractional form by introducing an augmented lyapunov functional an improved brl is established in terms of linear matrix inequalities lmis theoretical comparisons show that the obtained brl is less conservative than some previous ones and the recent results in s y xu l james y zou new results on delay dependent robust h control for systems with time varying delays automatica 42 2006 343 348 t li l guo c lin y m zhang delay range dependent bounded real lemma for time delay systems asian journal of control 10 2008 708 717 are actually equivalent to the special cases of the obtained brl numerical examples are also given to demonstrate the effectiveness of the proposed result

an lmi approach to h controller design for linear time delay systems we develop an output feedback h control design method for linear time delay systems based on the linear matrix inequality lmi approach a sufficient condition for the existence of h controllers of any order is given in terms of three lmis the condition is valid for both singular and regular problems we also give a characterization of h controllers in state space the lmi existence condition can be easily determined whether or not it is feasible and if it is then one can easily obtain h controller gain matrices via convex optimization

variable neighborhood search principles and applications abstract systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and effective metaheuristic for combinatorial and global optimization called variable neighborhood search vns we present a basic scheme for this purpose which can easily be implemented using any local search algorithm as a subroutine its effectiveness is illustrated by solving several classical combinatorial or global optimization problems moreover several extensions are proposed for solving large problem instances using vns within the successive approximation method yields a two level vns called variable neighborhood decomposition search vnds modifying the basic scheme to explore easily valleys far from the incumbent solution yields an efficient skewed vns svns heuristic finally we show how to stabilize column generation algorithms with help of vns and discuss various ways to use vns in graph theory i e to suggest disprove or give hints on how to prove conjectures an area where metaheuristics do not appear to have been applied before

path planning for multiple mobile robots under double warehouse abstract this work investigates the conflict free path planning problems for efficient guidance of multiple mobile robots under the dynamic double warehouse environment a challenging problem that appears recurrently in a wide range of applications such as the service robots moving in a multistory building united with two symmetrical transfer elevators double warehouse consists of two parallel warehouses within each warehouse the polynomial based paths are subject to constraints such as motion boundaries kinematics constraints obstacle avoidance limited resource of elevators and smoothness we formulate the shortest path planning problems as one time varying nonlinear programming problem tnlpp while restricted to the above constraints and apply the multi phase strategy to reduce their difficulty we present the new variant algorithms of pso named constriction factor and random perturb pso con per pso and the simulating annealing pso sa pso to achieve the solution numerical simulations verify that our approach can fulfill multiple mobile robots path planning problems under double warehouse successfully

modified quadratic discriminant functions and the application to chinese character recognition issues in the quadratic discriminant functions qdf are discussed and two types of modified quadratic disriminant functions mqdf1 mqdf2 which are less sensitive to the estimation error of the covariance matrices are proposed the mqdf1 is a function which employs a kind of a pseudo bayesian estimate of the covariance matrix instead of the maximum likelihood estimate ordinarily used in the qdf the mqdf2 is a variation of the mqdf1 to save the required computation time and storage two discriminant functions were applied to chinese character recognition to evaluate their effectiveness and remarkable improvement was observed in their performance

security based bi objective flow shop scheduling model and its hybrid genetic algorithm in this paper we considered the flow shop scheduling problem with respect to the both objectives of the makespan and the mean continuous running time proposed a security based bi objective flow shop scheduling model to solve the proposed model more effectively we presented a hybrid genetic algorithm hga which used some tailor made genetic operators and a local search operator in order to improve the local search ability of ga the proposed algorithm is tested with some well known problems in literature the computational results demonstrated the effectiveness of the proposed algorithm

diffusion controlled bimolecular reactions long and intermediate time regimes with imperfect trapping within a galanin approach we study a model for the a b ensuremath rightarrow b diffusion reaction system in one dimension in which the reaction sites are modeled by a markovian stochastic process mean values of the a particle density are obtained exactly for dilute b systems and approximately for dense ones results are compared with numerical simulations and show good agreement in the short intermediate and long time regimes

inference of differential equation models by genetic programming this paper describes an evolutionary method for identifying a causal model from the observed time series data we use a system of ordinary differential equations odes as the causal model this approach is known to be useful for practical applications e g bioinformatics chemical reaction models control theory etc to explore the search space more effectively in the course of evolution the right hand sides of odes are inferred by genetic programming gp and the least mean square lms method is used along with the ordinary gp we apply our method to several target tasks and empirically show how successfully gp infers the systems of odes we also describe an extension of the approach to the inference of differential equation systems with transcendental functions

decentralized adaptive output feedback control for a class of nonlinear large scale systems with unknown time varying delayed interactions in this paper the authors investigate a decentralized adaptive output feedback controller design for large scale nonlinear systems with input saturations and time delayed interconnections unmatched in control inputs the interaction terms with unknown time varying delays are bounded by unknown nonlinear bounding functions including all states of subsystems this point is a main contribution of this paper compared with previous output feedback control approaches which assume that the time delayed bounding functions only depend on measurable output variables the bounding functions are compensated by using appropriate lyapunov krasovskii functionals and the function approximation technique based on neural networks the observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem in addition we prove that all signals in the closed loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin finally an example is provided to illustrate the effectiveness of the proposed control system

gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems in this paper we propose gp coach a genetic programming based method for the learning of compact and accurate fuzzy rule based classification systems for high dimensional problems gp coach learns disjunctive normal form rules generated by means of a context free grammar coded as one rule per tree the population constitutes the rule base so it is a genetic cooperative competitive learning approach gp coach uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules the results obtained have been validated by the use of non parametric statistical tests showing a good performance in terms of accuracy and interpretability

enhancement of stochastic resonance the role of non gaussian noises abstract we have analyzed the phenomenon of stochastic resonance in a double well potential driven by a colored non gaussian noise using a path integral approach we have obtained a consistent markovian approximation that enables us to get through the two state theory analytical expressions for the signal to noise ratio finding an enhancement of this quantity when the system departs from gaussian behavior this finding is supported by extensive numerical simulations we discuss the relation of these results to some experiments in sensory systems

transitions induced by bounded noise abstract we study noise induced transitions when the system is driven separately by a gaussian colored noise and a non gaussian bounded noise with the same mean and stationary correlation clear differences in the nature of such transitions for these two cases are observed these transitions are shown to be enhanced essentially by the bounded noisy driving the results are obtained through a new approach based on numerical analysis of hyperbolic systems

a competitive and cooperative co evolutionary approach to multi objective particle swarm optimization algorithm design multi objective particle swarm optimization mopso is an optimization technique inspired by bird flocking which has been steadily gaining attention from the research community because of its high convergence speed on the other hand in the face of increasing complexity and dimensionality of today s application coupled with its tendency of premature convergence due to the high convergence speeds there is a need to improve the efficiency and effectiveness of mopso in this paper a competitive and cooperative co evolutionary approach is adapted for multi objective particle swarm optimization algorithm design which appears to have considerable potential for solving complex optimization problems by explicitly modeling the co evolution of competing and cooperating species the competitive and cooperative co evolution model helps to produce the reasonable problem decompositions by exploiting any correlation interdependency between components of the problem the proposed competitive and cooperative co evolutionary multi objective particle swarm optimization algorithm ccpso is validated through comparisons with existing state of the art multi objective algorithms using established benchmarks and metrics simulation results demonstrated that ccpso shows competitive if not better performance as compared to the other algorithms

stochastic resonance in ion channels characterized by information theory we identify a unifying measure for stochastic resonance sr in voltage dependent ion channels which comprises periodic conventional aperiodic and nonstationary sr within a simplest setting the gating dynamics is governed by two state conductance fluctuations which switch at random time points between two values the corresponding continuous time point process is analyzed by virtue of information theory in pursuing this goal we evaluate for our dynamics the tau information the mutual information and the rate of information gain as a main result we find an analytical formula for the rate of information gain that solely involves the probability of the two channel states and their noise averaged rates for small voltage signals it simplifies to a handy expression our findings are applied to study sr in a potassium channel we find that sr occurs only when the closed state is predominantly dwelled upon upon increasing the probability for the open channel state the application of an extra dose of noise monotonically deteriorates the rate of information gain i e no sr behavior occurs

a global local optimization approach to parameter estimation of rbf type models we present a hybrid global local optimization algorithm for parameter estimation of radial basis function rbf networks and the rbf type autoregressive models without exogenous inputs rbf ar or with exogenous inputs rbf arx the rbf ar x models are quasi linear time varying ar x models with gaussian rbf network style coefficients which have been used to effectively model the nonlinear behavior of various complex systems however the identification of these models is a difficult optimization problem because of the large number of local minima a hybrid approach is proposed in this paper to achieve better optimization results for these rbf type models the applied hybrid search strategy ea snpom is developed by combining an evolutionary algorithm ea with a gradient based algorithm known as the structured nonlinear parameter optimization method snpom this strategy makes use of the robustness of the ea to cover an entire global search space and the efficiency of the gradient search to converge to a local optimum several examples of time series modeling and system identification are presented the simulation results indicate that the performance of the proposed hybrid approach is better than the performance obtained from using each method ea or snpom alone furthermore the rbf ar x models estimated by the ea snpom achieve much better modeling accuracy relative to other neural networks or fuzzy models in the simulations

memetic multiobjective particle swarm optimization based radial basis function network for classification problems this paper presents a new multiobjective evolutionary algorithm applied to a radial basis function rbf network design based on multiobjective particle swarm optimization augmented with local search features the algorithm is named the memetic multiobjective particle swarm optimization rbf network mpson because it integrates the accuracy and structure of an rbf network the proposed algorithm is implemented on two class and multiclass pattern classification problems with one complex real problem the experimental results indicate that the proposed algorithm is viable and provides an effective means to design multiobjective rbf networks with good generalization capability and compact network structure the accuracy and complexity of the network obtained by the proposed algorithm are compared with the memetic non dominated sorting genetic algorithm based rbf network mgan through statistical tests this study shows that mpson generates rbf networks coming with an appropriate balance between accuracy and simplicity outperforming the other algorithms considered

a unified self stabilizing neural network algorithm for principal and minor components extraction recently many unified learning algorithms have been developed for principal component analysis and minor component analysis these unified algorithms can be used to extract principal components and if altered simply by the sign can also serve as a minor component extractor this is of practical significance in the implementations of algorithms this paper proposes a unified self stabilizing neural network learning algorithm for principal and minor components extraction and studies the stability of the proposed unified algorithm via the fixed point analysis method the proposed unified self stabilizing algorithm for principal and minor components extraction is extended for tracking the principal subspace ps and minor subspace ms the averaging differential equation and the energy function associated with the unified algorithm for tracking ps and ms are given it is shown that the averaging differential equation will globally asymptotically converge to an invariance set and the corresponding energy function exhibit a unique global minimum attained if and only if its state matrices span the ps or ms of the autocorrelation matrix of a vector data stream it is concluded that the proposed unified algorithm for tracking ps and ms can efficiently track an orthonormal basis of the ps or ms simulations are carried out to further illustrate the theoretical results achieved

effective markovian approximation for non gaussian noises a path integral approach abstract we have analyzed diffusion in a double well potential driven by a colored non gaussian noise using a path integral approach we have obtained a consistent markovian approximation to the initially non markovian problem such an approximation allows us to get analytical expressions for the mean first passage time that has been tested against extensive numerical simulations

bounded noise induced transitions in a tumor immune system interplay by studying a recent biophysical model of tumor growth in the presence of the immune system here we propose that the phenomenon of evasion of tumors from immune control at a temporal mesoscale might in some cases be due to random fluctuations in the levels of the immune system bounded noises are considered but the gaussian approach is also used for analytical reference after showing that in the case of bounded noises there may be multiple attractors in the space of probability densities we numerically show that the velocity of convergence toward asymptotic density is very slow and that a transitory analysis is needed then by simulations using the sine wiener and the tsallis noises we show that if the level of the noise is sufficiently large then there may be the onset of noise induced transitions in the transitory density evaluated at realistic times namely the transitions are from unimodal density centered at low values of tumor burden to bimodal densities that have a second maximum centered at higher values however those transitions depend on the distribution of the noise

effect of non gaussian noises on the stochastic resonance like phenomenon in gated traps we exploit a simple one dimensional trapping model introduced before prompted by the problem of ion current across a biological membrane the voltage sensitive channels are open or closed depending on the value taken by an external potential that has two contributions a deterministic periodic and a stochastic one here we assume that the noise source is colored and non gaussian with a q dependent probability distribution where q is a parameter indicating the departure from gaussianity we analyze the behavior of the oscillation amplitude as a function of both q and the noise correlation time the main result is that in addition to the resonant like maximum as a function of the noise intensity there is a new resonant maximum as a function of the parameter q

orthogonal least squares methods and their application to non linear system identification abstract identification algorithms based on the well known linear least squares methods of gaussian elimination cholesky decomposition classical gram schmidt modified gram schmidt householder transformation givens method and singular value decomposition are reviewed the classical gram schmidt modified gram schmidt and householder transformation algorithms are then extended to combine structure determination or which terms to include in the model and parameter estimation in a very simple and efficient manner for a class of multivariate discrete time non linear stochastic systems which are linear in the parameters

on the reachable set bounding of uncertain dynamic systems with time varying delays and disturbances in this paper improved conditions are proposed for finding an ellipsoidal bound of reachable sets which bound the state trajectories of uncertain dynamic systems with time varying delays and bounded peak disturbances a more generalized time derivative condition is considered in comparison with previous work by the use of convex hull properties and application of the lyapunov method sufficient conditions for finding an ellipsoidal bound of reachable sets of the concerned systems are established in terms of linear matrix inequalities lmis two numerical examples are included to show the superiority and effectiveness of our results

stochastic resonance in a tumor immune system subject to bounded noises and time delay abstract immunotherapy is one of the most recent approaches in cancer therapy a mathematical model of tumor immune interaction subject to a periodic immunotherapy treatment imitated by a periodic signal correlative and bounded stochastic fluctuations and time delays is investigated by numerical simulations for its signal power amplification spa within the tailored parameter regime the synchronous response of tumor growth to the immunotherapy stochastic resonance sr versus both the noises and delays is obtained the details are as follows i the peak values of spa versus the noise intensity a in the proliferation term of tumor cells decrease as the frequency of periodic signal increases i e an increase of the frequency restrains the sr ii an increase of the amplitude of periodic signal restrains the sr versus a but boosts up the sr versus the noise intensity b in the immune term iii there is an optimum cross correlated degree between the two bounded noises at which the system exhibits the strongest sr versus the delay time the reaction time of tumor cell population to their surrounding environment constraints iv upon increasing the delay time double sr versus the delay time the time taken by both the tumor antigen identification and tumor stimulated proliferation of effectors emerges these results may be helpful for an immunotherapy treatment for the sufferer

nonlinear regularization path for quadratic loss support vector machines regularization path algorithms have been proposed to deal with model selection problem in several machine learning approaches these algorithms allow computation of the entire path of solutions for every value of regularization parameter using the fact that their solution paths have piecewise linear form in this paper we extend the applicability of regularization path algorithm to a class of learning machines that have quadratic loss and quadratic penalty term this class contains several important learning machines such as squared hinge loss support vector machine svm and modified huber loss svm we first show that the solution paths of this class of learning machines have piecewise nonlinear form and piecewise segments between two breakpoints are characterized by a class of rational functions then we develop an algorithm that can efficiently follow the piecewise nonlinear path by solving these rational equations to solve these rational equations we use rational approximation technique with quadratic convergence rate and thus our algorithm can follow the nonlinear path much more precisely than existing approaches such as predictor corrector type nonlinear path approximation we show the algorithm performance on some artificial and real data sets

mahalanobis distance on extended grassmann manifolds for variational pattern analysis in pattern classification problems pattern variations are often modeled as a linear manifold or a low dimensional subspace conventional methods use such models and define a measure of similarity or dissimilarity however these similarity measures are deterministic and do not take into account the distribution of linear manifolds or low dimensional subspaces therefore if the distribution is not isotopic the distance measurements are not reliable as well as vector based distance measurement in the euclidean space we previously systematized the representations of variational patterns using the grassmann manifold and introduce the mahalanobis distance to the grassmann manifold as a natural extension of euclidean case in this paper we present two methods that flexibly extend the mahalanobis distance on the extended grassmann manifolds these methods can be used to measure pattern dis similarity on the basis of the pattern structure experimental evaluation of the performance of the proposed methods demonstrated that they exhibit a lower error classification rate

transitions induced by time delays and cross correlated sine wiener noises in a tumor immune system interplay abstract a model of tumor immune system interplay with time delays and cross correlated sine wiener noises is investigated by numerical simulations for the stationary probability distribution spd and stationary mean value x s t of tumor cell population results show that i the structure of the spd transfers from bimodal to unimodal as the reaction time of the cell population to environmental constraints increases ii conversely as the time of immune system to develop specific immune competence and the correlation time of noises increase the structure of the spd transfers from unimodal to bimodal iii as the cross correlated intensity between noises increases the transitions induced by are suppressed

technical communique new results on delay dependent robust h control for systems with time varying delays this paper considers the problem of delay dependent robust h control for uncertain systems with time varying delays an improved delay dependent bounded real lemma brl for time delay systems is established in terms of a linear matrix inequality based on the obtained brl a delay dependent condition for the existence of a state feedback controller which ensures asymptotic stability and a prescribed h performance level of the closed loop system for all admissible uncertainties is proposed in terms of a matrix inequality

estimating the support of a high dimensional distribution suppose you are given some data set drawn from an underlying probability distribution p and you want to estimate a simple subset s of input space such that the probability that a test point drawn from p lies outside of s equals some a priori specified value between 0 and 1 we propose a method to approach this problem by trying to estimate a function f that is positive on s and negative on the complement the functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data it is regularized by controlling the length of the weight vector in an associated feature space the expansion coefficients are found by solving a quadratic programming problem which we do by carrying out sequential optimization over pairs of input patterns we also provide a theoretical analysis of the statistical performance of our algorithm the algorithm is a natural extension of the support vector algorithm to the case of unlabeled data

eidetic wolf search algorithm with a global memory structure abstract a recently proposed metaheuristics called wolf search algorithm wsa has demonstrated its efficacy for various hard to solve optimization problems in this paper an improved version of wsa namely eidetic wsa with a global memory structure gms or just ewsa is presented ewsa makes use of gms for improving its search for the optimal fitness value by preventing mediocre visited places in the search space to be visited again in future iterations inherited from swarm intelligence search agents in ewsa and the traditional wsa merge into an optimal solution although the agents behave and make decisions autonomously heuristic information gathered from collective memory of the swarm search agents is stored in gms the heuristics eventually leads to faster convergence and improved optimal fitness the concept is similar to a hybrid metaheuristics based on wsa and tabu search ewsa is tested with seven standard optimization functions rigorously in particular ewsa is compared with two state of the art metaheuristics ant colony optimization aco and particle swarm optimization pso ewsa shares some similarity with both approaches with respect to directed random search the similarity with aco is however stronger as aco uses pheromones as global information references that allow a balance between using previous knowledge and exploring new solutions under comparable experimental settings identical population size and number of generations ewsa is shown to outperform both aco and pso with statistical significance when dedicating the same computation time only aco can be outperformed due to a comparably long run time per iteration of ewsa

enhanced speciation in particle swarm optimization for multi modal problems abstract in this paper we present a novel multi modal optimization algorithm for finding multiple local optima in objective function surfaces we build from species based particle swarm optimization spso by using deterministic sampling to generate new particles during the optimization process by implementing proximity based speciation coupled with speciation of isolated particles and by including turbulence regions around already found solutions to prevent unnecessary function evaluations instead of using error threshold values the new algorithm uses the particle s experience geometric mean and exclusion factor to detect local optima and stop the algorithm the performance of each extension is assessed with leave it out tests and the results are discussed we use the new algorithm called isolated speciation based particle swarm optimization ispso and a benchmark algorithm called niche particle swarm optimization nichepso to solve a six dimensional rainfall characterization problem for 192 rain gages across the united states we show why it is important to find multiple local optima for solving this real world complex problem by discussing its high multi modality solutions found by both algorithms are compared and we conclude that ispso is more reliable than nichepso at finding optima with a significantly lower objective function value

further improvement of free weighting matrices technique for systems with time varying delay a novel method is proposed in this note for stability analysis of systems with a time varying delay appropriate lyapunov functional and augmented lyapunov functional are introduced to establish some improved delay dependent stability criteria less conservative results are obtained by considering the additional useful terms which are ignored in previous methods when estimating the upper bound of the derivative of lyapunov functionals and introducing the new free weighting matrices the resulting criteria are extended to the stability analysis for uncertain systems with time varying structured uncertainties and polytopic type uncertainties numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

time series forecasting using a system of ordinary differential equations this paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations odes to predict the small time scale traffic measurements data we used the tree structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization pso algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations we also illustrate some experimental comparisons with genetic programming gene expression programming and a feedforward neural network optimized using pso algorithm experimental results reveal that the proposed method is feasible and efficient for forecasting the small scale traffic measurements data

a particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem abstract in this paper a particle swarm optimization algorithm pso is presented to solve the permutation flowshop sequencing problem pfsp with the objectives of minimizing makespan and the total flowtime of jobs for this purpose a heuristic rule called the smallest position value spv borrowed from the random key representation of bean j c bean genetic algorithm and random keys for sequencing and optimization orsa journal of computing 6 2 1994 154 160 was developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems in addition a very efficient local search called variable neighborhood search vns was embedded in the pso algorithm to solve the well known benchmark suites in the literature the pso algorithm was applied to both the 90 benchmark instances provided by taillard e taillard benchmarks for basic scheduling problems european journal of operational research 64 1993 278 285 and the 14 000 random narrow random and structured benchmark instances provided by watson et al j p watson l barbulescu l d whitley a e howe contrasting structured and random permutation flowshop scheduling problems search space topology and algorithm performance orsa journal of computing 14 2 2002 98 123 for makespan criterion the solution quality was evaluated according to the best known solutions provided either by taillard or watson et al the total flowtime criterion was evaluated with the best known solutions provided by liu and reeves j liu c r reeves constructive and composite heuristic solutions to the p ci scheduling problem european journal of operational research 132 2001 439 452 and rajendran and ziegler c rajendran h ziegler ant colony algorithms for permutation flowshop scheduling to minimize makespan total flowtime of jobs european journal of operational research 155 2 2004 426 438 for the total flowtime criterion 57 out of the 90 best known solutions reported by liu and reeves and rajendran and ziegler were improved whereas for the makespan criterion 195 out of the 800 best known solutions for the random and narrow random problems reported by watson et al were improved by the vns version of the pso algorithm

a discrete particle swarm optimization for lot streaming flowshop scheduling problem we consider an n job m machine lot streaming problem in a flowshop with equal size sublots where the objective is to minimize the total weighted earliness and tardiness to solve this problem we first propose a so called net benefit of movement nbm algorithm which is much more efficient than the existing linear programming model for obtaining the optimal starting and completion times of sublots for a given job sequence a new discrete particle swarm optimization dpso algorithm incorporating the nbm algorithm is then developed to search for the best sequence the new dpso improves the existing dpso by introducing an inheritance scheme inspired by a genetic algorithm into particles construction to verify the proposed dpso algorithm comparisons with the existing dpso algorithm and a hybrid genetic algorithm hga are made computational results show that the proposed dpso algorithm with a two point inheritance scheme is very competitive for the lot streaming flowshop scheduling problem

kernel projection classifiers with suppressing features of other classes we propose a new classification method based on a kernel technique called suppressed kernel sample space projection classifier sksp which is extended from kernel sample space projection classifier ksp in kernel methods samples are classified after they are mapped from an input space to a high dimensional space called a feature space the space that is spanned by samples of a class in a feature space is defined as a kernel sample space in ksp an unknown input vector is classified to the class of which projection norm onto the kernel sample space is maximized ksp can be interpreted as a special type of kernel principal component analysis kpca kpca is also used in classification problems however ksp has more useful properties compared with kpca and its accuracy is almost the same as or better than that of kpca classifier since ksp is a single class classifier it uses only self class samples for learning thus for a multiclass classification problem even though there are very many classes the computational cost does not change for each class however we expect that more useful features can be obtained for classification if samples from other classes are used by extending ksp to sksp the effects of other classes are suppressed and useful features can be extracted with an oblique projection experiments on two class classification problems indicate that sksp shows high accuracy in many classification problems

exponential family factors for bayesian factor analysis expressing data as linear functions of a small number of unknown variables is a useful approach employed by several classical data analysis methods e g factor analysis principal component analysis or latent semantic indexing these models represent the data using the product of two factors in practice one important concern is how to link the learned factors to relevant quantities in the context of the application to this end various specialized forms of the factors have been proposed to improve interpretability toward developing a unified view and clarifying the statistical significance of the specialized factors we propose a bayesian model family we employ exponential family distributions to specify various types of factors which provide a unified probabilistic formulation a gibbs sampling procedure is constructed as a general computation routine we verify the model by experiments in which the proposed model is shown to be effective in both emulating existing models and motivating new model designs for particular problem settings

input space versus feature space in kernel based methods this paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector sv kernel functions we first discuss the geometry of feature space in particular we review what is known about the shape of the image of input space under the feature space map and how this influences the capacity of sv methods following this we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel using the example of the class of inhomogeneous polynomial kernels which are often used in sv pattern recognition we then discuss the connection between feature space and input space by dealing with the question of how one can given some vector in feature space find a preimage exact or approximate in input space we describe algorithms to tackle this issue and show their utility in two applications of kernel methods first we use it to reduce the computational complexity of sv decision functions second we combine it with the kernel pca algorithm thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real world data

stochastic resonance induced by bounded noise and periodic signal in an asymmetric bistable system abstract stochastic resonance sr induced by the bounded noise in an asymmetric bistable system is investigated based on stochastic simulation the signal power amplification s p a is derived for the case of the additive modulated signal the simulation results indicate that 1 the sr phenomenon is weakened by the asymmetry of the system 2 increasing the frequency restricts the sr of the system 3 there is an optimum value of amplitude a matching the strongest sr phenomenon