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Analog VLSI implementation of wavelet transform using switched-current circuits

ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2012年第2期71卷 283-291页

作者： Li, Mu He, Yigang Long, Ying Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Sci & Technol Coll Informat & Elect Engn Xiangtan 410201 Peoples R China Changsha Univ Dept Elect & Commun Engn Changsha 410003 Hunan Peoples R China

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.

关键词： Switched-current circuits Wavelet filter Analog VLSI differential evolution algorithm Wavelet transform

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Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第4期39卷 4181-4189页

作者： Wang, Lin Fu, Qing-Liang Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Sch Informat Management Wuhan 430205 Hubei Peoples R China

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.

关键词： Continuous review inventory model Defuzzification Possibilistic mean value Fuzzy simulation differential evolution algorithm

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A comparative study of population-based optimization algorithms for turning operations

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INFORMATION SCIENCES 2012年 210卷 81-88页

作者： Yildiz, Ali R. Bursa Tech Univ Dept Mech Engn Bursa Turkey

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.

关键词： Multi-pass turning differential evolution algorithm Hybrid optimization

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A Novel non-Lyapunov way for detecting uncertain parameters of chaos system with random noises

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第2期39卷 1779-1783页

作者： Gao, Fei Qi, Yibo Balasingham, Ilangko Yin, Qiang Gao, Hongrui Wuhan Univ Technol Sch Sci Dept Math Wuhan 430070 Hubei Peoples R China Norwegian Univ Sci & Technol Dept Elect & Telecommun Signal Proc Grp N-7491 Trondheim Norway

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,

关键词： Chaos system Random noises Time-delay Parameters identification differential evolution algorithm

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adaptive fuzzy beamformer to steering vector mismatch

adaptive fuzzy beamformer to steering vector mismatch

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IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS)

作者： Hung, Jui-Chung Taipei Municipal Univ Educ Dept Comp Sci Taipei 100 Taiwan

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 differential evolution (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.

关键词： component adaptive beamformer differential evolution algorithm fuzzy systems first-order Taylor series

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A Probability Theory Based Intelligent QoS Multicast Routing algorithm

A Probability Theory Based Intelligent QoS Multicast Routing...

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Wang, Junwei Zhao, Yuhui NE Univ Qinhuangdao Dept Automat Qinhuangdao Hebei Province Peoples R China

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 (differential evolution), 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.

关键词： QoS Multicast Routing Probability Theory differential evolution algorithm Inaccurate Network Information

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DE-TDQL: An Adaptive Memetic algorithm

DE-TDQL: An Adaptive Memetic Algorithm

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IEEE Congress on evolutionary Computation (CEC)

作者： Bhowmik, Pavel Rakshit, Pratyusha Konar, Amit Kim, Eunjin Nagar, Atulya K. Jadavpur Univ ETCE Dept Kolkata India Univ North Dakota Dept Comp Sci Grand Forks ND 58202 USA Liverpool Hope Univ Dept Math & Comp Sci Liverpool Merseyside England

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.

关键词： memetic algorithm temporal difference q- learning differential evolution algorithm self adaptive differential evolution algorithm

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evolutionary Optimization algorithms Applied to Demand Dispatch via Stochastic Mixed-Integer- Programming

Evolutionary Optimization Algorithms Applied to Demand Dispa...

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IEEE Grenoble PowerTech Conference

作者： Thaís de Fátima Araújo Wadaed Uturbey Graduate Program in Electrical Engineering Federal University of Minas Gerais - UFMG Av. Ant?nio Carlos 6627 – Pampulha. 31.270-901 Belo Horizonte. MG Brazil Electrical Engineering Department Federal University of Minas Gerais - UFMG Av. Ant?nio Carlos 6627 – Pampulha. 31.270-901 Belo Horizonte. MG Brazil

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.

关键词： stochastic unit commitment demand dispatch evolutionary algorithms comparison particle swarm optimization differential evolution algorithm hybrid evolutionary algorithm

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A DE Based Variable Iterated Greedy algorithm for the No-Idle Permutation Flowshop Scheduling Problem with Total Flowtime Criterion

A DE Based Variable Iterated Greedy Algorithm for the No-Idl...

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7th International Conference on Intelligent Computing (ICIC)

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Wang, Ling Chen, Angela H. -L. Yasar Univ Dept Ind Engn Izmir Turkey Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Nanya Inst Technol Dept Finance Chungli 320 Taiwan

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.

关键词： differential evolution algorithm iterated greedy algorithm local search no-idle permutation flowshop scheduling problem

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Shape recognition based on neural networks trained by differential evolution algorithm

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NEUROCOMPUTING 2007年第4-6期70卷 896-903页

作者： Du, Ji-Xiang Huang, De-Shuang Wang, Xiao-Feng Gu, Xiao Chinese Acad Sci Hefei Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Peoples R China

In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differential evolution 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.

关键词： shape recognition generalization strategy differential evolution algorithm multiscale Fourier descriptors leaf image database

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