Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some clas...
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Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some classical method. The AR model is then reduced using the a state projection method, operating in the state space. The model reduction yields a reduced-order autoregressive moving-average (ARMA) model which interestingly preserves the key properties of the original full-order model such as causality, stability, minimality, and phase minimality. Line spectral frequencies (LSF) and signal-to-noise ratio (SNR) behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some computer simulations are conducted on some practical speech segments.
We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This fr...
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We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This fr...
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We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This freeway regularly suffers from shock waves originating mainly from on-ramps, and speed limits are now used to suppress these shock waves. First, we calibrate and validate the extended METANET model with data from the A1 freeway, and we use the Delft OD method to estimate the origin-destination patterns that are needed for the simulation of the destination oriented traffic. Next, we verify from data whether the necessary conditions for applying speed limits against shock waves are satisfied. We show that the MPC controller performs well even under the assumption that the traffic demand is not known on the on-ramps and is known for only a few kilometers upstream and downstream of the controlled stretch. This approach results in an improvement of the total time spent in the network with about 15%.
In this paper, we propose a deterministic approach to the robust filtering problem for a class of uncertain nonlinear systems. Based on polynomial Lyapunov functions and a relaxation technique, we derive linear matrix...
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ISBN:
(纸本)0972184449
In this paper, we propose a deterministic approach to the robust filtering problem for a class of uncertain nonlinear systems. Based on polynomial Lyapunov functions and a relaxation technique, we derive linear matrix inequalities conditions that minimize an upper-bound on the finite horizon 2-norm of the estimation error for all admissible uncertainty and input signals (including disturbances and measurement noise). Through a simple redefinition of the Lyapunov matrix, we extended the results for the reduced-order case without considering non-convex rank constraints.
We investigate necessary and sufficient conditions under which a nonlinear affine control system with outputs can be written as a gradient control system corresponding to some pseudo-Riemannian metric defined on the s...
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We investigate necessary and sufficient conditions under which a nonlinear affine control system with outputs can be written as a gradient control system corresponding to some pseudo-Riemannian metric defined on the state space. The results rely on a suitable notion of compatibility of the system with respect to a given affine connection, and on the input-output behavior of the prolonged system and the gradient extension. The symmetric product associated with an affine connection plays a key role in the discussion
Aboard current ships, such as the DDG 51, engineeringcontrol and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems ...
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Aboard current ships, such as the DDG 51, engineeringcontrol and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineeringcontrol involves seven to eight watchstanders at manned stations in the Central control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.
The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant co...
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The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics.
In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting pa...
In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.
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