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检索条件"主题词=EVOLUTIONARY PROGRAMMING"

共 651 条记录，以下是141-150 订阅

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Optimal conductor selection of radial distribution feeders using evolutionary programming

Optimal conductor selection of radial distribution feeders u...

引用

Conference on Convergent Technologies for the Asia-Pacific Region (TENCON 2003)

作者： Ranjan, R Chaturvedi, A Solanki, PS Das, D Multimedia Univ Melaka Malaysia

ISBN: (纸本)0780381629

In this paper a novel method for selecting optimal branch conductor of radial distribution feeders based on evolutionary programming (EP) has been presented. The aim of optimal conductor size selection is to select a feeder so as to minimize an objective function, which is sum of capital investment and capitalized energy loss costs. Optimal conductor type is determined for each feeder by using EP. Voltage constraints and maximum current carrying capacity of the conductors are also incorporated in the algorithm. One example is presented to demonstrate the effectiveness of the proposed method in the draft paper.

关键词： power distribution circuits optimal conductor selection evolutionary programming

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An evolutionary programming tool for assessing the operational value of distributed energy resources within restructured electricity industries

An evolutionary programming tool for assessing the operation...

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Australasian Universities Power Engineering Conference (AUPEC)

作者： MacGill, I. F. Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (纸本)9780646494883

There is growing world-wide and Australian interest in the greater potential role of distributed generation and demand-side resources within the electricity industry. These distributed resources can offer promising economic and environmental benefits for power system operation. There are considerable challenges, however, in developing modelling tools that can explore the operational value of such resources within restructured electricity industries. This paper describes a Dual evolutionary programming approach where software agents for power system resources co-evolve optimal operational behaviours over repeated power system simulations. The tool is applied to a simple case study exploring the potential operational synergies between significant PV penetrations and distributed energy storage options including controllable loads. The case study demonstrates this tool's capabilities in modelling the potentially complex operational behaviours of these distributed resources including stochastic PV outputs and loads with varying daily demand profiles, thermal energy storage, charging and discharging constraints and self-leakage.

关键词： distributed energy resources restructured electricity industries evolutionary programming power system simulation

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Tailored excitations for structural health monitoring via evolutionary programming

Tailored excitations for structural health monitoring via ev...

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Conference on Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems V

作者： Olson, Colin C. Todd, M. D. Univ Calif San Diego Dept Struct Engn La Jolla CA 92093 USA

ISBN: (纸本)0819462306

Dynamic interrogation of structures for the purposes of damage identification is an active area of research within the field of structural health monitoring with recent work focusing on the use of chaotic excitations and state-space analyses for improved damage detection. Inherent in this overall approach is the specific interaction between the chaotic input and the structure's eigenstate. The sensitivity to damage is theoretically enhanced by special tailoring of the input in terms of stability interaction with the structure. This work outlines the use of an evolutionary program to search the parameter space of a chaotic excitation for those parameters that are best suited to appropriately couple the excitation with the structure for enhanced damage detection. State-space damage identification metrics are used to detect damage in a computational model driven by excitations produced via the evolutionary program with non-optimized excitations used as comparison cases.

关键词： structural health monitoring chaos evolutionary programming gene expression programming

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A New Data Compression Technique Using an evolutionary programming Approach

引用

2nd International Multi-Topic Conference

作者： Ullah, Fahad Yahya, Khawaja M. Univ Engg & Technol Peshawar PK Dept Comp Syst Engn Peshawar Pakistan Univ Engn & Technol Peshawar Dept Elect Engn Peshawar Pakistan

ISBN: (纸本)9783642289613

Data compression is a necessary technique required in various scenarios these days from data communication to data storage. Text is an important form of data used ubiquitously in different communications and in computer world. This paper presents a novel data compression technique that uses an evolutionary programming approach for the compression process. Text is used as the experimental data in this research. By using evolution, the best compression method(s) are chosen in order to achieve maximum compression accuracy. For different experiments, the compression extent is measured and also the results are compared with the compression methods, individually. The results reveal the commendable performance of the system and the effect of evolution on the overall compression.

关键词： evolutionary programming genetic algorithms textual compression compression ratio

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Intelligent stock trading systems using fuzzy-neural networks and evolutionary programming methods

Intelligent stock trading systems using fuzzy-neural network...

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6th International Conference on Self-Formation Theory and Applications

作者： Simutis, R Masteika, S Vilnius State Univ Kaunas Fac Inst Socio Cultural Researches LT-3000 Kaunas Lithuania

ISBN: (纸本)3908450853

The goal of this study was to analyze the possibilities of fuzzy neural networks and evolutionary programming methods for creating the human skill based stock trading systems. In stock exchange markets, the relationships between market variables are generally too complex to make rightful trading decisions and to earn stabile profits using classical system theory approach. On the other hand, there are a lot of trading experts-practicians that successfully trade stocks and achieve good results in the stock exchange markets. A useful technique for expert-knowledge extraction is the supervised learning methods, where human-experts actions are mapped using fuzzy-neural networks. In this paper we outline this procedure. Also we discuss the possibilities for improvement the proposed human skill based stock trading systems. An efficient biological system evolves slowly over the course of hundreds and thousands of generations of individuals. Later generations have more fit and are more capable than earlier ones. Similarly, we have used evolutionary techniques to "evolve" the fuzzy-neural network based stock trading system, which is capable to solve the stock trading task more efficiently. Proposed procedure was tested using virtual trading system that uses historical data from US stock markets. The first results confirmed the good opportunities of the proposed approach.

关键词： expert systems fuzzy-neural networks evolutionary programming stock markets stock trading systems

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Hybridization of Meta-evolutionary programming and Artificial Neural Network for Predicting GridConnected Photovoltaic System Output

Hybridization of Meta-Evolutionary Programming and Artificia...

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1st IEEE Tencon Spring Conference for Region 10

作者： Sulaiman, Shahril Irwan Muhammad, Khairul Safuan Musirin, Ismail Shaari, Sulaiman Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia Univ Teknol MARA Fac Sci Appl Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781467363495

The unpredictable weather conditions has motivated the need of predicting the output of photovoltaic (PV) system. This paper presents a Grid-Connected Photovoltaic (GCPV) system output prediction scheme using hybridization of evolutionary programming (EP) and Artificial Neural Network (ANN). In this study, the AC kWh output of a GCPV system was predicted using ANN based on solar irradiance (SI) and PV module temperature (MT) as the inputs. In addition, a Meta-EP was hybridized with a Multi-Layer Feedforward Neural network (MLFNN) to search for the optimal number of neurons in hidden layer, the learning rate, the momentum rate, the type of activation function and the learning algorithm during ANN training such that the root mean square (RMSE) of the prediction could be minimized. Besides Meta-EP, other variations of EP were also tested for the hybridization with MLFNN such that the proposed Meta- EP could be justified. The results showed that Meta- EP based hybrid MLFNN (HMLFNN) had produced the lowest average RMSE, the lowest standard deviation (STD) and the lowest computation time during training when compared to other EP-based HMLFNNs. Similarly, during testing, the Meta- EP based HMLFNN had also outperformed the others in producing the lowest RMSE. In the comparisons, the coefficient of determination was found to be relatively very close to unity such that a high prediction performance could be ensured.

关键词： grid-connected photovoltaic prediction artificial neural network evolutionary programming root mean square error

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Power flow control in facts using evolutionary programming

Power flow control in facts using evolutionary programming

引用

1995 IEEE International Conference on evolutionary Computation (ICEC 95)

作者： Lai, LL Ma, JT CITY UNIV LONDON DEPT ELECT ELECTR & INFORMAT ENGNENERGY SYST GRPLONDON EC1V 0HBENGLAND

ISBN: (纸本)0780327594

This paper presents the use of an evolutionary programming (EP) to solve optimal power flow (OPF) problems in flexible AC transmission systems (FACTS). The unified power flow controller (UPFC) is used as a phase shifter and/or series compensator to regulate both angles and magnitude of branch voltages. EP, coupled with PQ power flow, selects the best regulation to minimize the real power loss and keep the power flows in their secure limits.

关键词： optimal power flow FACTS artificial intelligence evolutionary programming

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Power flow solvability identification and calculation algorithm using evolutionary programming technique

Power flow solvability identification and calculation algori...

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5th Student Conference on Research and Development

作者： Talib, Kahardi Muhammad Musirin, Ismail Kalil, Mohd. Rozely Idris, Mohamad Khayat IEEE Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9781424414697

Power flow study has been identified as the most important issue in power systems especially in the field of assessing the power system operability, survivability and also its security. It is considered as the back bone prior to further power system analysis, operation and planning. There have been various ready made products for power flow study packages in the market. Nonetheless, any attempts to solve power flow solution utilizing new developed algorithm or techniques can be considered as a brave trial. This paper proposes the evolutionary programming (EP) optimization technique to address the power flow problems through optimization technique. EP is based on the survivors of the fittest technique;where it is a sub-division of evolutionary Computation (EC) under the hierarchy of Artificial Intelligence (AI). Its capability in solving multi-variables, non-convex, non-linear and/or single or multi-objective optimization problems have been highlighted as the strength of EP. In realizing the effectiveness of EP in solving power problems, standard test system was utilized to ensure its workability in solving non-linear equations involving several pre-determined equality and inequality constraints equations. Results obtained from this study were compared with the existing established techniques;promising results were discovered implying that this technique is feasible to be implemented in addressing further optimization problems.

关键词： evolutionary programming power flow solution objective function optimization transmission loss

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The self-adaptive fuzzy neural network based on evolutionary programming

The self-adaptive fuzzy neural network based on evolutionary...

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6th International Conference on Machine Learning and Cybernetics

作者： Liu, Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing 100022 Peoples R China

ISBN: (纸本)9781424409723

In this paper, a approach for automatically generating fuzzy rules from sample patterns is presented. Firstly, with Cauchy mute operator and Gaussian mute operator, we propose a new evolutionary programming(EP) based on self-adaptive EP. Secondly, a self-adaptive fuzzy neural network is built based on the new evolutionary programming. In this method, structure identification and parameters estimation are performed automatically and simultaneously. The simulation results show that the proposed method in this paper can produce the compact and high performance fuzzy rule-base in comparison with other algorithms.

关键词： evolutionary programming fuzzy neural network

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The Neural Networks Ensembles solving Job Shop Schedule Problem based on evolutionary programming

The Neural Networks Ensembles solving Job Shop Schedule Prob...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Liu Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781424467129

A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process. In addition, the architecture of each NN in the ensemble and the size of the ensemble need not to be predefined. The Neural Networks Ensembles based on evolutionary programming is designed in order to solve Job Shop Schedule Problem. The simulation results show that the proposed method in this paper is valid.

关键词： neural networks ensemble evolutionary programming correlation learning

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