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检索条件"主题词=Optimization Algorithms"

共 4009 条记录，以下是3271-3280 订阅

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Prediction of customer buying nature from frequent itemsets generation using Quine-McCluskey method

Prediction of customer buying nature from frequent itemsets ...

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IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

作者： M. Krishnamurthy E. Rajalakshmi R Baskaran A. Kannan KCG College of Technology Chennai Tamil Nadu IN Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu IN Anna University Chennai Chennai Tamil Nadu IN

ISBN: (纸本)9781510804524

Finding frequent itemsets is one of the most important fields of data mining and also finding the rules associated with the itemset is another important ground in association rule mining. The process of finding new techniques to reduce candidate generation in order to generate frequent itemsets efficiently is a challenging task and generating rules in order to know the purchase behaviour of the customer to improve the business. In this paper, an efficient and optimized algorithm called Customer Purchase Behaviour (CPB) has been introduced for finding frequent itemsets using minimum scans, time and memory and the rules are generated. The subset invention process is used for the generation of frequent itemsets which reduces the intermediate tables. This approach reduces main memory requirement since it considers only a small cluster at a time. The purchase behaviour of the customer can be easily judged by using the Quine-McCluskey method. This algorithm is very efficient because of redundancy elimination and rule generation then compared with Apriori, Cluster-Based Bit Vector Mining for Association Rule Generation (CBVAR) and Improved Cluster-based Bit vector mining algorithm for Frequent Itemsets Generation (ICBV).

关键词： Cluster based mining Bit vector mining Redundancy elimination Frequent itemsets generation Customer Purchase Behaviour Quine-McCluskey Itemsets Association rules optimization algorithms learning (artificial intelligence) Information Extraction Rule FINDINGS mining data mining

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Implementation of controller optimization using S-function based metaheuristic algorithms

Implementation of controller optimization using S-function b...

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IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012)

作者： L.K. Letting J.L. Munda Y. Hamam Tshwane University of Technology Pretoria ZA

ISBN: (纸本)9781629939254

This paper presents general technique for implementing metaheuristic algorithms using S-Functions and its application in controller parameter optimization. The proposed method provides a suitable platform for parameter tuning in MATLAB/Simulink and PLECS software platforms. The proposed technique is suitable for exchanging optimization parameters when the objective function is not expressed in closed mathematical form. The S-Function based technique takes advantage of the software framework by embedding function calls of the optimization algorithm in callback methods which are executed at predefined time intervals. This leads to great improvement in algorithm efficiency in terms of simulation speed and ease of implementation. Validity of the proposed method is verified by implementing the particle swarm optimization algorithm (PSO) as an S-Function. The S-Function based PSO algorithm is applied to tune PI controllers for converter control in a doubly fed induction generator (DFIG) wind generation system.

关键词： Metaheuristic S-function Controller Parameter optimization Parameter optimization Controllers Software Frameworks algorithm efficiency optimization algorithms PI control Time intervals algorithms double-fed induction generator parametric subharmonic oscillator

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Testing k-Modal Distributions: Optimal algorithms via Reductions 13

Testing k-Modal Distributions: Optimal Algorithms via Reduct...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Constantinos Daskalakis Ilias Diakonikolas Rocco A. Servedio Gregory Valiant Paul Valiant MIT UC Berkeley Columbia University Microsoft Brown University

ISBN: (纸本)9781611972511

We give highly efficient algorithms, and almost matching lower bounds, for a range of basic statistical problems that involve testing and estimating the L_1 (total variation) distance between two k-modal distributions p and q over the discrete domain {1,..., n}. More precisely, we consider the following four problems: given sample access to an unknown k-modal distribution p, TESTING IDENTITY TO A KNOWN OR UNKNOWN DISTRIBUTION: 1. Determine whether p = q (for an explicitly given k- modal distribution q) versus p is ε-far from q; 2. Determine whether p = q (where q is available via sample access) versus p is ε-far from q; ESTIMATING L_1 DISTANCE ("TOLERANT TESTING") AGAINST A KNOWN OR UNKNOWN DISTRIBUTION: 3. Approximate d_(TV) (p, q) to within additive ∈ where q is an explicitly given k-modal distribution q; 4. Approximate d_(TV) (p, q) to within additive ∈ where q is available via sample access.

关键词： Testing k-Modal Distributions Optimal algorithms via Reductions optimization algorithms test problem sample complexity Unknown Access

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Gradient-Based Artificial Bee Colony for Damping Controllers Design

Gradient-Based Artificial Bee Colony for Damping Controllers...

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IEEE International Power Engineering and optimization Conference

作者： Mahdiyeh Eslami Hussain Shareef Azah Mohamed Mohammad Khajehzadeh Department of Electrical Electronic and Systems Engineering National University of Malaysia Department of Civil Engineering Anar Branch Islamic Azad University

ISBN: (纸本)9781467350723

A novel optimization algorithm by combining the artificial bee colony (ABC) algorithm and the sequential quadratic programming (SQP), that is the gradient based ABC algorithm, is presented to resolve the problems of global optimization and inter-area oscillations damping in power system. The proposed algorithm merges the global exploration ability of the artificial bee colony to converge quickly to a near optimum resolution, and the correct local exploitation capacity of the sequential quadratic programming to accelerate the search process and discover a correct solution. To show the feasibility and efficiency of the new method, numerical result is investigated on the New England system by tuning a power system stabilizer and a controller for the static VAR compensator. The proposed gradient based ABC algorithm is compared with ABC. The simulations studies demonstrate that the proposed algorithm based designed damping controllers perform better than controller designed by ABC.

关键词： Controllers activity based costing bee colonies Search process sequential quadratic programming Bee absorption of shock Power system stability New England optimization algorithms Global optimization power systems

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Accelerating model predictive control by online constraint removal

Accelerating model predictive control by online constraint r...

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IEEE Annual Conference on Decision and Control

作者： Michael Jost Martin Monnigmann Automatic Control and Systems Theory Ruhr-Universitat Bochum Bochum Germany

ISBN: (纸本)9781467357159

We propose a method for the acceleration of the online linear model predictive control (MPC) calculations with partial information on the explicit solution. We highlight two properties of the proposed approach: (i) It does not require to calculate the explicit solution first, and its computational effort grows only polynomially in the number of the constraints of the problem. The proposed approach can therefore be applied to problems that are too large for today's explicit MPC methods. (ii) The method is not based on a specific type or implementation of the optimization algorithm and can therefore easily be combined with a variety of existing MPC implementations. The proposed approach is, to the knowledge of the authors, one of yet a few attempts to use the insight into the structure of the explicit MPC law in online MPC.

关键词： Explicit Solution optimization algorithms acceleration Predictive control Model predictive control Constraint

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Predictive Current Control of a Four-Leg Indirect Matrix Converter with Imposed Source Currents and Common-Mode Voltage Reduction

Predictive Current Control of a Four-Leg Indirect Matrix Con...

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IEEE Energy Conversion Congress and Exposition

作者： C. Garcia M. Rivera M. Lopez J. Rodriguez P. Wheeler R. Pena J. Espinoza J. Riedemann Dep. of Electronics Eng. Universidad Técnica Federico Santa María Valparaíso CHILE Department of Industrial Technologies Universidad de Talca Curico CHILE Department of Elect. and Electronic Eng. University of Nottingham Nottingham U.K Department of Electrical Engineering Universidad de Concepción Concepción CHILE

ISBN: (纸本)9781479903375

This paper presents a new control strategy for a four-leg indirect matrix converter that effectively mitigates common-mode voltages and gives optimal control of source and load currents. This method uses the commutation state of the converter in the subsequent sampling time according to an optimization algorithm given by a simple cost function and a discrete system model. The control goals are the regulation of the output current according to an arbitrary reference and tracking of the source current reference, which is imposed in order to obtain sinusoidal waveforms with low distortion. The technique is enhanced by a reduction of the common-mode voltage using an extra term in the cost function to reduce early motor winding failure and bearing deterioration. Simulation results are presented to support the theoretical development.

关键词： converter subsequent output Converters common mode voltage matrix converters Current sources Output output current current reference control strategy sine waveform Cost functions optimization algorithms Current control

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Evolutionary Computation for Predicting Optimal Reaction Knockouts and Enzyme Modulation Strategies

Evolutionary Computation for Predicting Optimal Reaction Kno...

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IEEE Congress on Evolutionary Computation

作者： Pedro Evangelista Miguel Rocha Isabel Rocha IBB-Institute for Biotechnology and Bioengineering Centre of Biological Engineering Universidade do Minho CCTC - Computer Science and Technology Center Universidade do Minho

ISBN: (纸本)9781479904532

One of the main purposes of Metabolic Engineering is the quantitative prediction of cell behaviour under selected genetic modifications. These methods can then be used to support adequate strain optimization algorithms in a outer layer. The purpose of the present study is to explore methods in which dynamical models provide for phenotype simulation methods, that will be used as a basis for strain optimization algorithms to indicate enzyme under/over expression or deletion of a few reactions as to maximize the production of compounds with industrial interest. This work details the developed optimization algorithms, based on Evolutionary Computation approaches, to enhance the production of a target metabolite by finding an adequate set of reaction deletions or by changing the levels of expression of a set of enzymes. To properly evaluate the strains, the ratio of the flux value associated with the target metabolite divided by the wild-type counterpart was employed as a fitness function. The devised algorithms were applied to the maximization of Serine production by Escherichia coli, using a dynamic kinetic model of the central carbon metabolism. In this case study, the proposed algorithms reached a set of solutions with higher quality, as compared to the ones described in the literature using distinct optimization techniques.

关键词： evolutionary algorithm optimization algorithms carbon metabolism Enzymes KNOCKOUT metabolic engineering kinetic model

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A Quantized Consensus Algorithm for a Multi-Agent Assignment Problem

A Quantized Consensus Algorithm for a Multi-Agent Assignment...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Maria Pia Fanti Agostino Marcello Mangini Giovanni Pedroncelli Walter Ukovich Department of Electrical and Information Engineering Politecnico di Bari Bari Italy Department of Engineering and Architecture Università di Trieste Trieste Italy

ISBN: (纸本)9781479906505

This paper improves a previous result on the multiagent assignment problem, in which a group of agents has to reach a consensus on an optimal distribution of tasks, under communication and assignment constraints. However, the drawback of the proposed distributed algorithm was that the initial feasible assignment state is given. In this paper we develop a start-up algorithm to find an initial feasible assignment state based on synchronous communications among agents. Moreover, the agents exchange the messages and update autonomously and iteratively the task assignment. Some simulation results prove that the proposed consensus algorithm not only is able to reach a feasible solution but such a solution is close to the optimal one.

关键词： Agents and autonomous systems optimization algorithms Sensor networks optimization algorithms Sensor networks Autonomous Systems optimal distribution Agents distributed algorithms Feasible Solution

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Evolutionary Computation for Model Order Reduction with Parametric Generalised SPA

Evolutionary Computation for Model Order Reduction with Para...

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IEEE Annual Conference on Decision and Control

作者： Giovanni Muscato Maria Gabriella Xibilia DIEEI University of Catania viale A. Doria 6 95125 CATANIA Italy Department of Civil Computer Environmental Engineering and Applied Mathematics DICIEAMA University of Messina Messina Italy

ISBN: (纸本)9781467357159

In this paper evolutionary computation algorithms are applied to select optimal parameters in model order reduction for linear systems. In particular a parameterized set of reduced order model is obtained by using a Parametric Generalised Singular Perturbation Approximation of a balanced realization. The optimization algorithm is then used to select the parameter set that minimize a suitable performance index. Numerical examples are reported in comparison with other model order reduction methods.

关键词： Reduced order model evolutionary algorithm optimal parameter performance indicators Parametric optimization algorithms Linear system perturbation approximation

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A Robust MPC Approach to the Design of Behavioural Treatments

A Robust MPC Approach to the Design of Behavioural Treatment...

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IEEE Annual Conference on Decision and Control

作者： K. Bekiroglu C. Lagoa S. A. Murphy S. T. Lanza Department of Electrical Engineering Penn State University University Park PA 16802 USA Quantitative Methodology Program Institute for Social Research 2068 University of Michigan Ann Arbor MI 48106-1248 The Methodology Center The Pennsylvania State University 204 E. Calder Way Suite 400 State College PA 16801

ISBN: (纸本)9781467357159

The objective of this paper is to provide some initial results on the application of control tools to the problem treatment design. Human behavior and reaction to treatment is complex and dependent on many unmeasurable external stimuli. Therefore, to the best of our knowledge, it cannot be described by simple models. Hence, one of the main messages in this paper is that, to design a treatment (controller) one cannot rely on exact models. More precisely, to be able to design effective treatments, we propose to use "simple" uncertain affine models whose response "covers" the most probable subject responses. So, we propose a simple model that contains two different types of uncertainties: one aimed at uncertainty of the dynamics and another aimed at approximating external perturbations that patients face in their daily life. With this model at hand, we design a robust model predictive controller, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms.

关键词： optimization algorithms Uncertainty Special structures Behavioural Hand

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