This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control t...
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This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control the diversification by adopting a simple mechanism for random selection in ACO. The proposed method is tested on the 10-unit test system with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, two types of the proposed diversity control technique have the features of easy implementation and a better convergence rate superior to a standard ACO.
In order to support multimedia communication, it is necessary to develop routing algorithms that make decisions based on multiple Quality of Service (QoS) parameters. This is because new services such as video on dema...
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In order to support multimedia communication, it is necessary to develop routing algorithms that make decisions based on multiple Quality of Service (QoS) parameters. This is because new services such as video on demand and remote meeting systems have multiple QoS requirements. However, the problem of QoS routing is difficult because finding a feasible route with two independent path constraints is NP-complete problem. Also, QoS routing algorithms for broadband networks must be adaptive, flexible, and intelligent for efficient network management. In this paper, we propose a multi-purpose optimization method for QoS routing based on Genetic Algorithm (GA). The simulation results show that the proposed method has a good performance and therefore is a promising method for QoS routing.
With recent advances in wireless communication technology, mobile computing is an importance research area. Mobile IP is designed to provide IP services to roaming nodes. Mobile users take advantage of this protocol t...
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When the process uncertainty size increases, even linear minimum phase systems must sacrifice desirable aggressive feedback control benefits to avoid an excessive 'cost of feedback', while preserving the robus...
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Tracking feedback control trade-offs increase dangerously with arbitrarily large uncertainties in the process mode!. This is shown by the bounds in the QFT domain. In this context, the paper develops a theory on the c...
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This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the...
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An intelligent lumber grading system was developed to provide a new way for estimating the strength of a board by posing the estimation problem as an empirical learning problem. This system processed the X-ray image, ...
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An intelligent lumber grading system was developed to provide a new way for estimating the strength of a board by posing the estimation problem as an empirical learning problem. This system processed the X-ray image, extracted geometric features (of 1000 boards that eventually underwent destructive strength testing), and predicted the strength of the lumber by using a neural network. The X-ray image was passed through a threshold filter to separate the knots based on the fact that a denser knot produces a local maximum (as a rounded protrusion in an otherwise almost flat density surface) of the X-ray image. Each knot was modeled by a three-dimensional-cone with seven parameters. Information on all the detected knots such as volume, and knot-area-ratio were fed to a processor to generate 16 geometrical features (such as; the average of knot area ratio, and the number of knots detected in each board), which characterize each board. Then by using back-propagation as the training method, cross correlation as the measure of accuracy, and actual strength of a thousand boards as the empirical data set, a neural network was trained to estimate the strength of each board. The learning system consisted of three layers, with 1, 5, 16 neurons in output, hidden and input layer respectively. Ten-fold cross validation was used to produce an unbiased accuracy of the estimation problem. The learning and testing sets comprised of 900 and 100 boards respectively. By repeating the learning and testing for ten times and averaging the results, a coefficient of determination of 0.4059 was reached in this study for using X-ray images alone. The same methodology was applied to MOE (modulus of elasticity) and a coefficient of determination of 0.56 was reached. The results were improved by fusing the X-ray image and MOE using a learning system consisting of three layers, with 1, 5, 40 neurons in output, hidden and input layer respectively. Ten-Fold cross validation resulted in a coefficient o
A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memo...
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ISBN:
(纸本)0780377362
A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to cope with delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that:(1) only a delayed reward is used, (2) some of S-R pairs are preprogrammed, (3) immediate reward is possible, and (4) our KP method is applied.
This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the...
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This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the system non-linearity is incorporated into model parameters by using non-linear non-parametric models (NNMs). Kullback discrimination Information (KDI) is introduced as fault detection index to evaluate the distortion in identified model, which is caused by a fault. The effectiveness of the method is verified through simulation studies on the ship propulsion system.
Tracking feedback control trade-offs increase dangerously with arbitrarily large uncertainties in the process model. This is shown by the bounds in the QFT domain. In this context, the paper develops a theory on the c...
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Tracking feedback control trade-offs increase dangerously with arbitrarily large uncertainties in the process model. This is shown by the bounds in the QFT domain. In this context, the paper develops a theory on the contribution of the plant uncertainty to the tracking QFT bounds through a serious study of tracking bound formulas. From this, a methodology for uncertainty fragmentation is developed. It hopes to loosen the bound toughness, or the control design trade-offs. Better feedback benefits can be achieved while reducing the high-frequency amplification of noises and disturbances at the plant input at the same time. Different QFT controllers in a controller-scheduler structure should drive different uncertainty boundaries.
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