For applications requiring low-voltage low-power and real-time processing, a novel scheme for the VLSI implementation of wavelet transform (WT) using switched-current (SI) circuits is presented. SI circuits are well s...
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For applications requiring low-voltage low-power and real-time processing, a novel scheme for the VLSI implementation of wavelet transform (WT) using switched-current (SI) circuits is presented. SI circuits are well suited for these applications since the dilation constant across different scales of the transform can be implemented, and controlled by both the aspect-ratio of the transistors and the clock frequency. The quality of such implementation depends on the accuracy of the corresponding wavelet approximation. First, an optimized procedure based on differential evolution algorithm (DE) is applied to approximate the transfer function of a linear steady-state system whose impulse response is the required wavelet. The proposed approach significantly improves the accuracy of approximation wavelets. Next, the approximation of time-domain wavelet function is implemented by the SI analog filters. Finally, the design of the complete SI filter based on first-order and biquad section as main building block is detailed. Simulations demonstrate the performance of the proposed approach to analog WT implementation.
In this paper, continuous review inventory models in which a fraction of demand is backordered and the remaining fraction is lost during the stock out period are considered under fuzzy demands. In order to find the op...
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In this paper, continuous review inventory models in which a fraction of demand is backordered and the remaining fraction is lost during the stock out period are considered under fuzzy demands. In order to find the optimal decision under different situations, two decision methods are proposed. The first one is finding a minimum value of the expected annual total cost, and the second one is maximizing the credibility of an event that the total cost in the planning periods does not exceed a certain budget level. For the first decision method, an approach of ranking fuzzy numbers by their possibilistic mean value is adopted to achieve the optimal solution. For the second one, the technique of fuzzy simulation and differential evolution algorithms are integrated to design hybrid intelligent algorithms to solve the fuzzy models. Subsequently, the two decision models are compared and some advices about inventory cash flow management are given. Further, sensitivity analysis is conducted to give more general situations to illustrate the rationality of the management advices. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
In manufacturing industry, turning operations are used to remove unwanted sections of a part to obtain the final product. In this paper a comparison of state-of-the-art optimization techniques to solve multi-pass turn...
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In manufacturing industry, turning operations are used to remove unwanted sections of a part to obtain the final product. In this paper a comparison of state-of-the-art optimization techniques to solve multi-pass turning optimization problems is presented. Furthermore, a hybrid technique based on differential evolution algorithm is introduced for solving manufacturing optimization problems. The results have demonstrated the superiority of the hybrid approach over the other techniques like artificial bee colony algorithm, differential evolution algorithm, hybrid particle swarm optimization algorithm, hybrid artificial immune-hill climbing algorithm, hybrid taguchi-harmony search algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm and an improved simulated annealing algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required. (C) 2012 Elsevier Inc. All rights reserved.
The paper is concerned with the uncertain parameters and time-delays of chaos system with random noises. A scheme based on differential evolution algorithm (DE) is newly introduced to solve the problem via a nonnegati...
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The paper is concerned with the uncertain parameters and time-delays of chaos system with random noises. A scheme based on differential evolution algorithm (DE) is newly introduced to solve the problem via a nonnegative multi-modal nonlinear optimization, which finds a best combination of parameters and time-delays such that an objective function is minimized. The illustrative examples, in both systems free of time-delays and time-delays systems with random noises, are given to demonstrate the validity of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved,
This paper proposed an efficient beamformer to deal with the signal steering vector mismatches. The empirical results show that the significant performance degradation occurs when the signal steering vector for the de...
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ISBN:
(纸本)9781467350839
This paper proposed an efficient beamformer to deal with the signal steering vector mismatches. The empirical results show that the significant performance degradation occurs when the signal steering vector for the desired signal is not known exactly. Thus, we design adaptive fuzzy beamformer, which adopt the method of fuzzy systems to modify the value of signal steering vector by first-order Taylor expansion. In this situation, the beamformer design problem becomes a complicated nonlinear estimation problem. In this study, a design method based on differentialevolution (DE) algorithm is proposed to treat the adaptive fuzzy beamformer design problem. Finally, the simulation confirm that the proposed scheme performance is significantly improved and preferable robustness if the effect of point error of desired signal is considered along with the use of expert knowledge enabled by the beamformer.
In this paper, taking the characteristics of NGI (Next Generation Internet) into account, and base on its network model and mathematical model, a probability theory based intelligent QoS multicast routing algorithm is...
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ISBN:
(纸本)9783037853122
In this paper, taking the characteristics of NGI (Next Generation Internet) into account, and base on its network model and mathematical model, a probability theory based intelligent QoS multicast routing algorithm is presented. Under inaccurate information of QoS parameters, using fast searching ability of DE (differentialevolution), the proposed algorithm tries to find a multicast routing tree with the maximum probability of meeting with QoS requirement under the given cost. Simulation results have shown that the proposed algorithm is both feasible and effective.
Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical differentialevolution algorit...
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ISBN:
(纸本)9781467315098
Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical differential evolution algorithm and Q-learning is used to construct the memetic algorithm. Computer simulation with standard benchmark functions reveals that the proposed memetic algorithm outperforms three distinct differential evolution algorithms.
This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a ...
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ISBN:
(纸本)9781467356688
This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that the grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. The load not dispatched by the operator is not known with certainty, and is represented as a stochastic parameter in the optimization problem. Consumption restrictions associated with flexible demand are modeled by equality energy constraints. The performance of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, is presented.
In this paper, we present a variable iterated greedy (vIGP_DE) algorithm where its parameters (basically destruction size and cooling parameter for the simulated annealing type of acceptance criterion) are optimized b...
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ISBN:
(纸本)9783642259432
In this paper, we present a variable iterated greedy (vIGP_DE) algorithm where its parameters (basically destruction size and cooling parameter for the simulated annealing type of acceptance criterion) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented such that first chromosome represents the destruction size and cooling parameter of the iterated greedy algorithm while second chromosome is simply a permutation assigned to each individual in the population randomly. As an application area, we choose to solve the no-idle permutation tlowshop scheduling problem with the total flowtime criterion. To the best of our knowledge, the no-idle permutation flowshop problem hasn't yet been studied thought it's a variant of the well-known permutation flowshop scheduling problem. The performance of the vIGP_DE algorithm is tested on the Tail lard's benchmark suite and compared to a very recent variable iterated greedy algorithm from the existing literature. The computational results show its highly competitive performance and ultimately, we provide the best known solutions for the total flowtime criterion for the Tail lard's benchmark suit.
In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differentialevolution training algorithm is proposed. Firstly, a generalization strategy of differential evo...
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In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differentialevolution training algorithm is proposed. Firstly, a generalization strategy of differential evolution algorithm is introduced. And this global optimization algorithm is applied to train the multilayer perceptron neural networks. The proposed algorithms are evaluated through a plant species identification task involving 25 plant species. For this practical problem, a multiscale Fourier descriptors (MFDs) method is applied to the plant images to extract shape features. Finally, the experimental results show that our proposed GDE training method is feasible and efficient for large-scale shape recognition problem. Moreover, the experimental results illustrated that the GDE training algorithm combined with gradient-based training algorithms will achieve better convergence performance. (c) 2006 Elsevier B.V. All rights reserved.
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