Showing papers in "International Journal of Systems Science in 1997"
TL;DR: The results show that Manners model is not only the best formulation for both job-shop and flow- shop problems, but is also the best for the permutation flow-shop problem.
Abstract: With the advances of powerful computer capacity and efficient integer programming software, mathematical programming-based scheduling research is beginning to receive more and more attention from researchers. Although it is not an efficient solution method, mathematical programming formulation is a natural way to attack scheduling problems. The purpose of this paper is to present a study of five existing integer programming formulations for job-shop, flow-shop and permutation flow-shop scheduling problems, and a comparison of their model sizes for each particular setting. The results show that Manners model is not only the best formulation for both job-shop and flow-shop problems, but is also the best for the permutation flow-shop problem.
108 citations
TL;DR: An LMls characterization of guaranteed ℋ∞ andℋ2 norms costs for linear systems with convex bounded parameter uncertainties for continuous-time and discrete-time systems is proposed.
Abstract: This paper proposes an LMls characterization of guaranteed ℋ∞ and ℋ2 norms costs for linear systems with convex bounded parameter uncertainties. Both continuous-time and discrete-time systems are addressed.
75 citations
TL;DR: The stability of linear neutral delay-differential systems with multiple delays is investigated and some simple stability criteria through the evaluation of the measure and norm of corresponding matrices are presented.
Abstract: The stability of linear neutral delay-differential systems with multiple delays is investigated, which is an extension of the work reported in the literature. The case of delay-independent stability is considered. Some simple stability criteria through the evaluation of the measure and norm of corresponding matrices are presented.
74 citations
TL;DR: Evaluating the efficiency of academic units is a difficult problem to deal with, due to the problems inherent in measuring the inputs and outputs of the system and to the potential implications of the resulting resource allocations in the budget reduction exercises in place at many educational establishments.
Abstract: The primary purpose of this paper is to examine an important public issue, namely how the use of a particular fixed budget formula impacts on the input/output structure of the decision-making units directly affected by its implementation. As an example, it assesses short- and long-term effects on the efficiency of academic units of the University of New Brunswick of the budget formula used by the Maritime Provinces Higher Education Commission ( MPHEC) of Canada in the resource allocation process to the universities in its midst. Evaluating the efficiency of academic units is a difficult problem to deal with, due to the problems inherent in measuring the inputs and outputs of the system and to the potential implications of the resulting resource allocations in the budget reduction exercises in place at many educational establishments. The contribution of Data Envelopment Analysis to the solution of this problem is evaluated. Practical and research implications are discussed.
71 citations
TL;DR: This work develops a general qualitative framework for identifying the possible ways of combining the universality of neural networks with the prior knowledge and experience embedded in the available physical models, model-based estimators, conventional controllers, as well as vague linguistic descriptions provided by human experts about the system behaviour and manual control strategies.
Abstract: Whereas some researchers in the control community see neural networks as a panacea for solving all realistic control-related problems, another school rejects the neural-network paradigm altogether in favour of well-established conventional schemes with a sound theoretical basis. Much of the promising research using neural networks for modelling, prediction and control, however, realizes the value of, and the need for, both paradigms, and exploits their mutual complementarity to address realistic problem situations. This work develops a general qualitative framework for identifying the possible ways of combining the universality of neural networks with the prior knowledge and experience embedded in the available physical models, model-based estimators, conventional controllers, as well as vague linguistic descriptions provided by human experts about the system behaviour and manual control strategies. The presented framework not only naturally leads to the previously proposed schemes in the literature, but ...
70 citations
TL;DR: In this article, many ways to classify image texture and many approaches split the problem into three categories: texture analysis, texture classification, texture segmentation, and texture analysis with respect to image textures.
Abstract: Texture analysis has found wide application in, say, remote sensing, medical diagnosis, and quality control. There are many ways to classify image texture and many approaches split the problem into...
69 citations
TL;DR: Within the framework of the newly designed genetic algorithm, the NP-hard classic job-shop scheduling problem can be efficiently solved with high quality and the optimal solutions of the two famous benchmarks are found.
Abstract: Job-shop scheduling is essentially an ordering problem. A new encoding scheme for a classic job-shop scheduling problem is presented, by which a schedule directly corresponds to an ordering string. For the new encoding, a simple but highly effective crossover operation is contrived, and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classic job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the two famous benchmarks, the Fisher and Thompson's 10 × 10 and 20 × 5 problems, are found.
69 citations
TL;DR: It is shown that asynchronous round-based and fault-tolerant protocols cannot be transformed into protocols that are simultaneously fault-Tolerant and self-stabilizing (ftss), as is otherwise possible in the synchronous mode of computation.
Abstract: This paper focuses on protocols that are simultaneously resilient to permanent failures (crash faults) and transient failures (memory and message corruption). First, we show that asynchronous round-based and fault-tolerant protocols cannot be transformed into protocols that are simultaneously fault-tolerant and self-stabilizing (ftss), as is otherwise possible in the synchronous mode of computation. Secondly, we show that it is impossible to find the number of processes (i.e. the size) on a family of networks, as it has been proven for the ring network. Finally, we present a ftss protocol for solving ring size by assuming that each process accesses a failure detector. We also propose two self-stabilizing implementations for the failure detector that differ in their degree of tolerance to transient failures.
55 citations
TL;DR: In six illustrative examples, it is shown that all of the stability robustness bounds obtained by the proposed methods are larger than the existing ones in the literature.
Abstract: This paper presents the robustness bounds of delay-independent asymptotic stability for linear systems with multiple time-varying delayed perturbations including both nonlinear delayed perturbations and linear unstructured/structured perturbations in the delayed states. The case of arbitrary unknown but constant delays is included in the results as a special case. All of the obtained bounds can be maximized by choosing some appropriate free parameter matrices. In six illustrative examples, it is shown that all of the stability robustness bounds obtained by the proposed methods are larger than the existing ones in the literature.
54 citations
TL;DR: Two heuristic models for the inventory replenishment problem in which the demand rate, deterioration rate, ordering cost, holding cost and shortage cost are all assumed to vary with time are developed and analysed.
Abstract: In this article, we develop and analyse two heuristic models for the inventory replenishment problem in which the demand rate, deterioration rate, ordering cost, holding cost and shortage cost are all assumed to vary with time. Shortages in inventory are allowed and are completely backlogged. Our objective is to determine the optimal replenishment policy of the inventory system whether or not a finite planning horizon exists. Both the heuristic models are illustrated with the help of a numerical example. Finally, the sensitivity analysis is also presented for the heuristic rule that outperforms the other one.
52 citations
TL;DR: The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the available measured output and the given noise and uncertainty description.
Abstract: The paper considers the problem of robust state estimation for the case in which some of the measurement data is missing. This problem is considered within the framework of a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the available measured output and the given noise and uncertainty description.
TL;DR: The problem addressed is the construction of a perturbating upper bound on uncertain noise covariances so as to guarantee that the deviation of the estimate error performance index remains within the precision prescribed in actual problems.
Abstract: This paper deals with the problem of a robust filter design for discrete-time descriptor systems with uncertain noise. The problem addressed is the construction of a perturbating upper bound on uncertain noise covariances so as to guarantee that the deviation of the estimate error performance index remains within the precision prescribed in actual problems. Furthermore, the worst performance in the uncertain case can be minimized by a minimax robust filter.
TL;DR: It is demonstrated that ultimate bounded tracking may be achieved with bounded inputs despite the presence of uncertainty.
Abstract: This paper considers the development of tracking controllers for a multi-input nonlinear non-minimum phase Planar Vertical Take-off and Landing (PVTOL)aircraft. The system is considered to be subject to bounded/sector additive external uncertainties. A dynamic sliding mode control philosophy is employed. The stability and robustness of the resulting closed-loop system is analysed. It is demonstrated that ultimate bounded tracking may be achieved with bounded inputs despite the presence of uncertainty. © 1997 Taylor & Francis Group, LLC.
TL;DR: It is pointed out that the boundary conditions of the results of Cao et al, (1996a) should be explicitly indicated in the theorem and several new results about quadratic stability of continuous-time fuzzy control systems are given.
Abstract: Further results about the quadratic stability of continuous-time fuzzy control systems are presented in this paper. We first point out that the boundary conditions of the results of Cao et al, (1996a) should be explicitly indicated in the theorem. Then, several new results about quadratic stability of continuous-time fuzzy control systems are given. All of these results address the boundary conditions.
TL;DR: A realistic approach for software reliability growth modelling incorporating the joint effect of test effort and learning factor and maximum likelihood estimates of the reliability growth parameters are obtained using a numerical method.
Abstract: This paper presents a realistic approach for software reliability growth modelling incorporating the joint effect of test effort and learning factor. The error detection process in software is described using a non-homogeneous Poisson process. Maximum likelihood estimates of the reliability growth parameters are obtained using a numerical method. Optimal release policies for cost and reliability are discussed. Finally, an example using real-life data is presented for illustration and comparison
TL;DR: Simulation results from a well-known example are used to demonstrate that simple modelling and accurate in prediction are the merits of the proposed methodology.
Abstract: The application of genetic algorithms to the identification of a fuzzy grey model is investigated. Based on a few past output data, the next output from the unknown plant can be predicted by the basic grey model. To improve the accuracy of the prediction model, a fuzzy controller is designed to determine the quantity of compensation for the output from the grey system. Genetic algorithms are used to optimize the roughly determined fuzzy model. A test pattern is then fed to the well-tuned fuzzy system to infer the quantity of compensation through the centre of gravity defuzzification method. The procedures of identifying three different types of fuzzy models are presented. Simulation results from a well-known example are used to demonstrate that simple modelling and accurate in prediction are the merits of the proposed methodology.
TL;DR: The derived neurofuzzy network is applied to state estimation in which the system model identified is converted to a fuzzy logic system and the neural network identification technique and the Kalman filter are merged to achieve optimal adaptive filtering and prediction for unknown but observable nonlinear processes.
Abstract: A fuzzy logic system has been shown capable of arbitrarily approximating any nonlinear function, and has been successfully applied to system modelling. The functional rule fuzzy system enables the input-output relation of the fuzzy logic system to be analysed. B-spline basis functions have many desirable numerical properties and as such can be used as membership functions of fuzzy system. This paper analyses the input-output relation of a fuzzy system with afunctional rule base and B-spline basis functions as membership functions, constructing a neurofuzzy network for systems representation in which the training algorithm is very simple since the network is linear in the weights. It is also desired to merge the neural network identification technique and the Kalman filter to achieve optimal adaptive filtering and prediction for unknown but observable nonlinear processes. In this paper the derived neurofuzzy network is applied to state estimation in which the system model identified is converted t...
TL;DR: It has been found that the network not only compresses the data, but also improves the quality of retrieved ECG signal with respect to elimination of high-frequency interference present in the original signal.
Abstract: The paper deals with an efficient composite method which has been developed for data compression, signal retrieval and feature extraction of ECG signals. After carrying out detailed studies and by training different topologies of error-back-propagation (EBP) artificial neural network (ANN) with respect to variations in number of hidden layers and number of elements in each hidden layer, the best topology with two hidden layers and four elements in each hidden layer has been finalized for ECG data compression using a Military Hospital (MH) data base. Thereafter, detailed investigations have been carried out with a network topology of 335-4-4-335 using CSE data base. After signal retrieval from the compressed data, it has been found that the network not only compresses the data, but also improves the quality of retrieved ECG signal with respect to elimination of high-frequency interference present in the original signal. The compression ratio (CR) in ANN method increases with increase in number of ECG cycle...
TL;DR: Five dominance properties for the precedence relations between jobs in an optimal solution are proposed and a sharp lower bound on the total tardiness of a subproblem is derived in the branch-and-bound algorithm to facilitate the search for an optimal schedule.
Abstract: In this paper, the problem of sequencing jobs on a two-machine flowshop to minimize total tardiness is considered, and a branch-and-bound algorithm is developed. Five dominance properties for the precedence relations between jobs in an optimal solution are proposed and a sharp lower bound on the total tardiness of a subproblem is derived. The dominance properties and the lower bound are implemented in the branch-and-bound algorithm to facilitate the search for an optimal schedule. Computational experiments are conducted and the results show that this algorithm is more efficient than the existing ones.
TL;DR: A direct adaptive controller based on high-order neural networks (HONNs) is presented to solve the tracking control problem for a general class of unknown nonlinear systems.
Abstract: A direct adaptive controller based on high-order neural networks (HONNs) is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The plant is assumed to be a feedback linearizable and minimum-phase system. Firstly, an ideal implicit feedback linearization control (IFLC) is established using implicit function theory. Then a HONN is applied to construct this IFLC to realize approximate linearization. The proposed controller ensures that the closed-loop system is Lyapunov stable and that the tracking error converges to a small neighbourhood of the origin. The requirements of an off-line training phase and the persistant excitation condition are eliminated. Simulation results verify the effectiveness of the proposed controller and the theoretical discussion.
TL;DR: A Predictive Neural Network (PNN) based technique to detect QRS complexes of electrocardiograms (ECGs) using the back propagation algorithm on non-QRS portions of the ECG to predict the signal one-step ahead.
Abstract: This paper describes a Predictive Neural Network (PNN) based technique to detect QRS complexes of electrocardiograms (ECGs). The PNN is trained, using the back propagation algorithm, on non-QRS portions of the ECG to predict the signal one-step ahead. High prediction error is then taken as an indication of the occurrence of a QRS complex. A simple peak detection logic is then invoked to mark the exact location and magnitude of either a Q- or an R- or an S- peak within the QRS complex. The performance of the detector software is illustrated with examples representative of different QRS morphologies. The accuracy of QRS complex detection has been tested using bipolar standard limb leads of a standard ECG library; a sensitivity of 98·96% has been achieved. A brief discussion on how well this technique performs in comparison with the other QRS detectors is also presented.
TL;DR: The result shows that the Tensile strength and the Brinell hardness of a material are highly correlated and the accuracy of indirect measurement of the tensile strength is satisfactory.
Abstract: This paper studies the dynamic relationship between hardness and tensile strength of a material, through which we evaluate the tensile strength of the material for a higher temperature from the corresponding experimental data of Brinell hardness. The tensile strength of a material based on temperature is important for mechanical engineering design. It is harder and takes more time to obtain experimental data of the tensile strength than to obtain those data of the Brinell hardness of a material. The prediction model DGDM( 1, 1, I) is employed in this study. The series of Brinell hardness at equispaced intervals of temperature is considered as the leading indicator ( the input series) while the series of tensile strength of the material at the corresponding temperature is taken as the output series. The result shows that the tensile strength and the Brinell hardness of a material are highly correlated and the accuracy of indirect measurement of the tensile strength is satisfactory.
TL;DR: This paper investigates the control design problem of vehicle following systems with actuator delay and builds a constant bound for the time delay to guarantee the individual vehicle stability and proves that the proposed control laws can achieve zero steady state errors.
Abstract: Actuator delays can substantially degrade the performance of vehicle following control. In this paper, we investigate the control design problem of vehicle following systems with actuator delay. The main requirement in designing such systems is to ensure the stability of the individual vehicle and that there are no slinky-effects in the platooning. Based on Lyapounov's method, we first construct a constant bound for the time delay to guarantee the individual vehicle stability. Next, we derive a condition for achieving no slinky-effects. Finally, we prove that the proposed control laws can achieve zero steady state errors,
TL;DR: New sufficient conditions are derived which only require the knowledge of the continuous trajectory at the time instants when switching occurs and the Lyapunov function is required to be non-increasing only along each subsequence of the switching.
Abstract: The stability of hybrid dynamic systems in the sense of Lyapunov is considered. New sufficient conditions are derived which only require the knowledge of the continuous trajectory at the time instants when switching occurs. The Lyapunov function is required to be non-increasing only along each subsequence of the switching. These sufficient conditions are used to study a switched server system, a manufacturing system and a three-states hybrid dynamic system.
TL;DR: A new method for the identification of the nonlinear Hammerstein model, consisting of a static linearity in cascade with a linear dynamic part, is introduced that makes use of the well-known nonlinear mapping ability of MFNN and avoids the restrictive assumptions of the previous identification methods.
Abstract: A new method for the identification of the nonlinear Hammerstein model, consisting of a static linearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modelled by a multilayer feedforward neural network (MFNN) and the linear part is modelled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the MFNN and the parameters of the ARMA. The new method makes use of the well-known nonlinear mapping ability of MFNN and avoids the restrictive assumptions of the previous identification methods. Two numerical examples are presented to illustrate the performance of the developed model and recursive algorithm.
TL;DR: It is proven that the composite optimal control can achieve a performance which is O(ee2) close to the optimal performance and the equivalence between the new composite optimal controller and the existing one is established for the standard singularly perturbed systems.
Abstract: In this paper, a new method, based on a generalized algebraic Riccati equation arising in descriptor systems, is presented to solve the composite optimal control problem of singularly perturbed systems. Contrary to the existing method, the slow subsystem is viewed as a special kind of descriptor system. A new composite optimal controller is obtained which is valid for both standard and non-standard singularly perturbed systems. It is shown that the composite optimal control can be obtained simply by revising the solution of the slow regulator problem. It is proven that the composite optimal control can achieve a performance which is O(ee2) close to the optimal performance. Although this result is well-known for the standard singularly perturbed systems, it is new in the non-standard case. The equivalence between the new composite optimal controller and the existing one is also established for the standard singularly perturbed systems.
TL;DR: A local modelling-based approach to nonlinear state estimation using a Sugeno fuzzy inference framework is presented, and simulation results presented suggest potential improvements when compared with conventional extended Kalman filtering.
Abstract: A local modelling-based approach to nonlinear state estimation using a Sugeno fuzzy inference framework is presented. Four new fuzzy Kalman filters are proposed on this basis, and simulation results presented suggest potential improvements when compared with conventional extended Kalman filtering.
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TL;DR: Simulation and experimental results show that the dynamic behaviours of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances.
Abstract: The dynamic motion of an adaptive controlled slider-crank mechanism, which is driven by a permanent magnet ( PM) synchronous servo-motor, is studied. First, the mathematical model of the motor-mechanism coupling system is developed, where Hamilton's principle and the Lagrange multiplier method are applied to formulate the equation of motion. Then, by using the stability analysis with inertia-related Lyapunov function, an adaptive controller for the motor-mechanism coupling system is obtained. Simulation and experimental results show that the dynamic behaviours of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances.
TL;DR: This paper proposes a condition under which the designed state feedback gain is guaranteed to be real and the specified state covariance X of the closed loop system is also achieved.
Abstract: By using the describing function approximation method, the covariance control of a multivariable stochastic system with hysteresis nonlinearities is studied. Since the describing function for a hysteresis nonlinearity is always a complex form, this complex component in the system makes the problem more complicated. This paper proposes a condition under which the designed state feedback gain is guaranteed to be real and the specified state covariance X of the closed loop system is also achieved.