The paper formulates the static control problem of Markov jump linear systems, assuming that the controller does not have access to the jump variable. We derive the expression of the gradient for the cost motivated by...
详细信息
The paper formulates the static control problem of Markov jump linear systems, assuming that the controller does not have access to the jump variable. We derive the expression of the gradient for the cost motivated by the evaluation of 10 gradient-based optimization techniques. The numerical efficiency of these techniques is verified by using the data obtained from practical experiments. The corresponding solution is used to design a scheme to control the velocity of a real-time DC motor device subject to abrupt power failures. Copyright (c) 2014 John Wiley & Sons, Ltd.
A new model for a viable battery swapping station is proposed to minimize its cost by determining the optimized charging schedule for swapped electric vehicle (EV) batteries. The aim is to minimize an objective functi...
详细信息
A new model for a viable battery swapping station is proposed to minimize its cost by determining the optimized charging schedule for swapped electric vehicle (EV) batteries. The aim is to minimize an objective function considering three factors: the number of batteries taken from stock to serve all the swapping orders from incoming EVs, potential charging damage with the use of high-rate chargers, and electricity cost for different time period of the day. A mathematical model is formulated for the charging process following the constant-current/constant-voltage charging strategy. An integrated algorithm is proposed to determine an optimal charging schedule, which is inspired by genetic algorithm, differential evolution, and particle swarm optimization. A series of simulation studies are executed to assess the feasibility of the proposed model and compare the performance between the proposed algorithm and the typical evolutionary algorithms.
A method to optimize the separation in micellar EKC (MEKC) of mixtures of acidic compounds as a function of two parameters, pH and concentration of sodium dodecyl sulfate, has been developed. The method considers the ...
详细信息
A method to optimize the separation in micellar EKC (MEKC) of mixtures of acidic compounds as a function of two parameters, pH and concentration of sodium dodecyl sulfate, has been developed. The method considers the prediction of the retention time and the shape of the peaks. The retention time is predicted from the retention factor model and the peak shape by a polynomically modified Gaussian function that considers peak width, asymmetry factor, and height. An algorithm to calculate the global resolution of the separation at any experimental pH and [SDS] has been applied. This algorithm provides a 3-D resolution map to easily detect the areas in which resolution for the separation of the compounds is maximum. Initial experiments to fit the models have been performed with a set of ten phenolic compounds with different hydrophobicities and pK(a) values, and therefore, expected to behave in a different way with changes of pH and surfactant concentration. The experiments encompassed a pH range from 6.7 to 11.1, and a sodium dodecyl sulfate concentration range from 40 to 80 mM. Through the proposed methodology, chromatograms have been simulated at different pH and [SDS] very accurately. Furthermore, the resolution at any experimental point within the studied ranges have been also calculated, giving an optimum resolution value at pH 6.7 and [SDS] = 72 mM.
This article deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication, without any central coordinator. We...
详细信息
This article deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication, without any central coordinator. We propose the gradient tracking with an adaptive momentum estimation (GTAdam) distributed algorithm, which combines a gradient tracking mechanism with first- and second-order momentum estimates of the gradient. The algorithm is analyzed in the online setting for strongly convex cost functions with Lipschitz continuous gradients. We provide an upper bound for the dynamic regret given by a term related to the initial conditions and another term related to the temporal variations of the objective functions. Moreover, a linear convergence rate is guaranteed in the static setup. The algorithm is tested on a time-varying classification problem, on a (moving) target localization problem, and in a stochastic optimization setup from image classification. In these numerical experiments from multiagent learning, GTAdam outperforms state-of-the-art distributed optimization methods.
This article presents a general approach to design and optimize printed ultrawideband (UWB) antennas by using invasive weed optimization (IWO), a well-known global optimization algorithm. To achieve the required radia...
详细信息
This article presents a general approach to design and optimize printed ultrawideband (UWB) antennas by using invasive weed optimization (IWO), a well-known global optimization algorithm. To achieve the required radiation parameters over a wide bandwidth, a frequencyrelated cost function with optimal weighting coefficients is suggested. Two prototypes of the optimized antenna have been manufactured and examined. The experimental outcomes show good agreement with the simulated ones that validate the proposed optimization approach. The optimized antenna has a compact size of 50 mm × 50 mm. The operational bandwidth of the antenna for S11 < -10 dB, from both measurement and simulation, is 150%, based on the center frequency of 6.4 GHz (1.6-11.2 GHz), which also covers the UWB (3.1-10.6 GHz) applications. The time-domain response of the antenna was also investigated by measurement of the group delay. Finally, the efficiency of the optimized antenna in terms of bandwidth, size, and gain are compared with a number of previously proposed designs. Comparison results show that the optimized antenna outperforms other designs cited. The experimental outcomes in frequency as well as the time domain show that the antenna is suitable for use in UWB or other communication systems.
The productivity of automated production lines depends on the velocities of operating robot manipulators. Hence, time-optimal control for working cycles of robot manipulators are of decisive importance. In this paper,...
详细信息
The productivity of automated production lines depends on the velocities of operating robot manipulators. Hence, time-optimal control for working cycles of robot manipulators are of decisive importance. In this paper, the minimum-time retraction of a robot arm, subject to gravity and suspended on a prismatic-rotational joint, is investigated. Iterative integration methods using a B-spline representation for the actuator force and torque, a Runge-Kutta integration method, and a sequential quadratic programming optimization algorithm are used to calculate the time-optimal control and trajectories. Results show that nonlinear gravity and centrifugal effects are exploited very effectively, to obtain minimum-time maneuvers from an initial to a final state. These states also determine the switching structures of the control. It is demonstrated that even the simple retraction of a robot arm produces unexpectedly complex time-optimal solutions.
This paper introduces a new optimization algorithm for the minimization of the time sidelobes of the correlation function of a pseudonoise (PN) sequence by applying dynamic weighting to the sequence. The resulting opt...
详细信息
This paper introduces a new optimization algorithm for the minimization of the time sidelobes of the correlation function of a pseudonoise (PN) sequence by applying dynamic weighting to the sequence. The resulting optimized time sidelobe level sequences are to be used ill direct sequence spread spectrum (DS-SS) systems with digital modulations such as BPSK, DPSK, QPSK, etc. The new optimization algorithm starts with a PN sequence. It first optimizes the correlation time sidelobes for the case where the consecutive data bits are identical (11 or 00), It then optimizes the correlation time sidelobes for the case of alternating consecutive data bits (10 or 01), The suppressed time sidelobe level sequences are derived by iterating these algorithms alternately starting from the initial PN sequence. The derived suppressed time sidelobe sequences show excellent correlation characteristics when compared to conventional PN sequences such as maximal length sequences, Gold sequences and Barker codes. Surface acoustic wave (SAW) devices were used to implement the optimized time sidelobe level sequences in a matched filter pair. The design of the apodized SAW-matched filters and their predicted second order effects are presented. The experimental results for the SAW-matched filters for the optimized time sidelobe level sequences derived from a Barker code were found to be in good agreement with the theoretical predictions from this new algorithm.
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the...
详细信息
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution;as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is
We propose a modification to the simultaneous perturbation stochastic approximation (SPSA) methods based on the comparisons made between the first- and second-order SPSA (I SPSA and 2SPSA) algorithms from the perspect...
详细信息
We propose a modification to the simultaneous perturbation stochastic approximation (SPSA) methods based on the comparisons made between the first- and second-order SPSA (I SPSA and 2SPSA) algorithms from the perspective of loss function Hessian. At finite iterations, the accuracy of the algorithm depends on the matrix conditioning of the loss function Hessian. The error of 2SPSA algorithm for a loss function with an ill-conditioned Hessian is greater than the one with a well-conditioned Hessian. On the other hand, the I SPSA algorithm is less sensitive to the matrix conditioning of loss function Hessians. The modified 2SPSA (M2SPSA) eliminates the error amplification caused by the inversion of an ill-conditioned Hessian. This leads to significant improvements in its algorithm efficiency in problems with an ill-conditioned Hessian matrix. Asymptotically, the efficiency analysis shows that M2SPSA is also superior to 2SPSA in a large parameter domain. It is shown that the ratio of the mean square errors for M2SPSA to 2SPSA is always less than one except for a perfectly conditioned Hessian or for an asymptotically optimal setting of the gain sequence. Copyright (C) 2002 John Wiley Sons, Ltd.
A model of a rugate coating that takes into account production potentialities of the Leybold Syrus Pro 1100 deposition system is presented. An efficient algorithm for the synthesis of rugate coatings is proposed. Nume...
详细信息
A model of a rugate coating that takes into account production potentialities of the Leybold Syrus Pro 1100 deposition system is presented. An efficient algorithm for the synthesis of rugate coatings is proposed. Numerical results are also presented. (c) 2006 Optical Society of America.
暂无评论