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Optimize BpNN Using New breeder genetic algorithm 2nd

2nd International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Alabbas, Maytham Jaf, Sardar Abdullah, Abdul-Hussein M. Univ Basrah Coll CS & IT Dept Comp Sci Basrah Iraq Univ Durham Sch Engn & Comp Sci Durham England Univ Basrah Coll Sci Dept Comp Sci Basrah Iraq

ISBN: (纸本)9783319483085;9783319483078

In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is investigated. The multi-layer network (MLN) is taken into account as the ANN structure to be optimized. The idea presented here is to use the genetic algorithms to yield contemporaneously the optimization of: (1) the design of NN architecture in terms of number of hidden layers and of number of neurons in each layer;and (2) the choice of the best parameters (learning rate, momentum term, activation functions, and order of training patterns) for the effective solution of the actual problem to be faced. The back-propagation (BP) algorithm, which is one of the best-known training methods for ANNs, is used. To verify the efficiency of the current scheme, a new version of the breeder genetic algorithm (NBGA) is proposed and used for the automatic synthesis of NN. Finally, several problems of the experiment were taken and the results show that the back-propagation neural network (BpNN) classifier improved the current scheme has higher accuracy of classification and greater gradient of convergence than other classifiers, which have been proposed in the literature.

关键词： breeder genetic algorithm Back-propagation network Artificial neural network Multi-layer neural network

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Efficient Analysis of Electromagnetic Scattering in Post-Wall Waveguides and Its Application to Optimization of Millimeter Wave Filters

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2020年 1卷 448-455页

作者： Akopian, Arkadi Burduli, Guga Jandieri, Vakhtang Maeda, Hiroshi Hong, Wonbin Omar, Ahmed Abdelmottaleb Yasumoto, Kiyotoshi Werner, Douglas H. Erni, Daniel Free Univ Tbilisi Dept Elect & Comp Engn GE-0159 Tbilisi Georgia Univ Duisburg Essen Fac Engn Gen & Theoret Elect Engn ATE D-47057 Duisburg Germany CENIDE Ctr Nanointegrat Duisburg Essen D-47048 Duisburg Germany Fukuoka Inst Technol Dept Informat & Commun Engn Fukuoka 8110295 Japan Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan Penn State Univ Dept Elect Engn University Pk PA 16802 USA

Functional post-wall waveguides and waveguide based compact passive circuits - namely bandpass filters - that are formed by introducing additional metallic or dielectric posts inside the post-wall waveguide are analyzed. Scattering parameters (S-parameters) of the waveguide-based bandpass filters are calculated using the method of images combined with the lattice sums technique. The developed formulation is numerically very fast, hence it can be considered as one of the best-suited approaches for the analysis of multiple scattering in post-wall waveguides and for numerical structural optimization. A breeder genetic algorithm is employed as a global search heuristic in order to track down the optimal geometrical parameters for the introduced posts yielding a bandpass filter characteristic in the desired frequency bands. Comparison with other references validates the correctness and utility of our formalism. Different configurations for the specifications of post-wall waveguide-based bandpass filters are discussed.

关键词： Scattering periodic structure bandpass filter breeder genetic algorithm modal analysis post-wall waveguide

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Reduction in the torque ripple of a vertical axis wind turbine through foil pitching optimization

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WIND ENGINEERING 2020年第2期44卷 115-124页

作者： Erfort, Gareth von Backstrom, Theodor W. Venter, Gerhard Stellenbosch Univ Dept Mech & Mechatron Engn Private Bag XI ZA-7602 Stellenbosch South Africa

Vertical axis wind turbines have a place in the small scale renewable energy market. They are not currently implemented on a commercial scale but have found a niche space in urban areas. Here, the turbulent wind conditions and limited space are more easily tapped into with a vertical axis wind turbine. However, the challenges facing these types of turbines have hampered deployment. One of these issues is the fluctuating torque experienced during operation, which can lead to over-designed power trains. genetic- and gradient-based optimization is applied to an analytical model of a vertical axis wind turbine, in order to reduce the torque fluctuation while attempting to maintain a high power coefficient. The reduction in torque ripple is achieved through a sinusoidal pitching motion of the blades. The torque ripple can be reduced by 10% with a similar reduction in power coefficient.

关键词： Vertical axis wind turbine optimization pitch breeder genetic algorithm power quality

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Numerical Study and Optimization of Post-Wall Waveguides and Filters for Millimeter Waves

Numerical Study and Optimization of Post-Wall Waveguides and...

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German Microwave Conference (GeMiC)

作者： Akopian, Arkadi Burduli, Guga Jandieri, Vakhtang Maeda, Hiroshi Hong, Wonbin Yasumoto, Kiyotoshi Erni, Daniel Free Univ Tbilisi Sch Math & Comp Sci 240 Agmashenebeli Ave GE-0159 Tbilisi Georgia Univ Duisburg Essen Gen & Theoret Elect Engn ATE Fac Engn D-47048 Duisburg Germany Fukuoka Inst Technol Dept Informat & Commun Engn Fukuoka 8110295 Japan Pohang Univ Sci & Technol POSTECH Pohang 37673 South Korea Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan

ISBN: (纸本)9783982039718

Functional post-wall waveguides and waveguide based compact passive circuits - namely bandpass filters - that are formed by introducing additional metallic or dielectric posts inside the post-wall waveguide are analyzed. The propagation constant of a post-wall waveguide is experimentally measured and compared with the results obtained using the originally developed numerical method. A good agreement is observed in the whole frequency range. The S-parameters of the waveguide-based bandpass filters are calculated using the image theory combined with the lattice sums technique. The developed formulation is numerically fast, hence it can be considered as one of the best-suited approach for structural topological optimization. Finally, a breeder genetic algorithm is used as a global search heuristic in order to track down the optimal geometrical parameters for the introduced posts yielding a bandpass filter characteristic in the desired frequency bands.

关键词： post-wall waveguide bandpass filters breeder genetic algorithm modal analysis

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Accelerated multi-gravitational search algorithm for size optimization of truss structures

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 109-119页

作者： Khatibinia, Mohsen Yazdani, Hessam Univ Birjand Dept Civil Engn Birjand Iran Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA

Weak local exploitation capability of the gravitational search algorithm (GSA) and its slow convergence rate in final iterations have been demonstrated in the literature. This paper presents a modified GSA denoted here as the accelerated multi-gravitational search algorithm (AMGSA) that exhibits an improved convergence rate. In AMGSA, the simplex crossover (SPX) and the operator mutation of the breeder genetic algorithm (BGA) are incorporated with the multi-gravitational search algorithm (MGSA) to achieve an algorithm with a good exploration-exploitation balance. MGSA is adopted to prevent stagnation of the search into a local optimum (i.e. to improve the exploration capability), while the SPX and the BGA mutation operator are used to bias the search toward promising areas of the search space (i.e. to promote local exploitation). The performance of AMGSA is evaluated using several benchmark truss optimization examples. Results indicate that AMGSA not only exhibits an improved balance between the exploration and exploitation schemes but also shows competitive promise in effectively and efficiently solving large-scale optimization problems as it requires a significantly lower number of structural analyses compared to other algorithms that it is checked against.

关键词： Size optimization Truss structures Gravitational search algorithm Simplex crossover breeder genetic algorithm

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breeder hybrid algorithm approach for natural gas demand forecasting model

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ENERGY 2017年 141卷 1269-1284页

作者： Karadede, Yusuf Ozdemir, Gultekin Aydemir, Erdal Suleyman Demirel Univ Engn Fac Dept Ind Engn TR-32260 Isparta Turkey Kafkas Univ Fac Engn & Architecture Dept Ind Engn TR-36100 Kars Turkey

A breeder hybrid algorithm consisting of the constitution of nonlinear regression-based breeder genetic algorithm and simulated annealing is proposed for the objective of forecasting the natural gas demand with a smaller error rate. The main aim of this study is to show general natural gas demand forecasting model of the breeder hybrid algorithm based nonlinear regression. The most important difference that distinguishes this natural gas demand forecasting model from other models in the literature is that the proposed model evolves continuously with the best solutions in both the breeder genetic algorithm and simulated annealing parts. It is applied to Turkey natural gas demand forecasting to show its superiority and applicability. The consumption amount of natural gas has between 1985 and 2000 is determined as dependent variable whereas the independent variables are determined as the gross national product, population and the growth rate. Then, the consumption amounts of natural gas between 2001 and 2014 are forecasted with significantly small MAPE values that are obtained 0.0188 and 0.0143 for year 2014 using the proposed algorithms and compared to different solutions in the literature. The proposed algorithms are superior to the comparable algorithms in the literature. Then, two scenarios are applied for the years between 2015 and 2030 for future projection. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Natural gas demand forecasting Nonlinear forecasting model breeder genetic algorithm Simulated annealing breeder hybrid algorithm

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breeder genetic algorithm for Power System Stabilizer Design

Breeder Genetic Algorithm for Power System Stabilizer Design

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2010 IEEE World Congress on Computational Intelligence

作者： Sheetekela, Severus Folly, Komla Agbenyo Univ Cape Town Dept Elect Engn ZA-7925 Cape Town South Africa

ISBN: (纸本)9781424481262

This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary algorithm (EA) techniques, namely;genetic algorithm (GA) and breeder genetic algorithm. BGA is a new form of evolutionary algorithm, which is based on the idea of survival of the fittest, but differs from the traditional genetic algorithm due to its artificial breeding nature. An eigenvalue based objective function is used in the design of the PSSs whereby the algorithms maximize the lowest damping ratio over specified operating conditions. For comparison purpose, the Conventional PSS (CPSS) is also included. The performance and effectiveness of the PSSs in damping the electromechanical modes is investigated. Eigenvalue analysis and time domain simulations show that BGA-PSS and GA-PSS perform better than the CPSS for all the operating conditions considered except at the nominal operating condition. However, BGA-PSS performs slightly better than the GA-PSS.

关键词： breeder genetic algorithm eigenvalue based objective function eigenvalues and eigenfunctions electromechanical modes evolutionary algorithm genetic algorithms power system stability power system stabilizer design time domain simulations

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The design of a composite wavelet matched filter for face recognition using breeder genetic algorithm

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OPTICS AND LASERS IN ENGINEERING 2005年第12期43卷 1277-1291页

作者： Sinha, A Singh, K Indian Inst Technol Dept Phys New Delhi 110016 India

We describe a method using the breeder genetic algorithm to design a composite wavelet matched filter for face recognition. The designed filter can be used in an optical correlator to separate two classes of input face images. Mexican hat and Morlet wavelets have been used for the design of the filter. The performance of the filter has also been evaluated in the presence of additive white Gaussian noise. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： face recognition wavelets matched filter composite wavelet matched filter breeder genetic algorithm Morlet wavelet Mexican hat wavelet Gaussian noise

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Deterministic and evolutionary extraction of delta-lognormal parameters: Performance comparison

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2007年第1期21卷 21-41页

作者： Djioua, Moussa Plamondon, Rejean Della Cioppa, Antonio Marcelli, Angelo Ecole Polytech Lab Scribens Dept Genie Elect Montreal PQ H3C 3A7 Canada Univ Salerno Dipartimento Ingn Informaz & Ingn Elettr Nat Computat Lab I-84084 Fisciano SA Italy

A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model has been very useful for proposing original solutions to various pattern recognition problems (signature segmentation and veri. cation, handwriting analysis and synthesis, etc.). Most of these applications rely on the use of an efficient algorithm to extract the delta-lognormal parameters from real data with the best possible fit. In this paper, we compare two such algorithms: a deterministic one, based on nonlinear regression, and a breeder genetic algorithm. The performance of these two algorithms and of their combinations are compared using the same artificial database, composed of analytical delta-lognormal profiles and their noisy versions (20 dB SNR). In the free-noise case, the analysis of the experimental results shows that the deterministic approach leads to better results than the evolutionary one, while under the extremely noisy conditions selected, the evolutionary approach seems to be less sensitive to noise, but is nevertheless less successful than the deterministic search.

关键词： handwriting generation simulated movement delta-lognormal model parameter extraction nonlinear regression Levenberg-Marquardt algorithm stochastic extraction breeder genetic algorithm artificial noise

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Deterministic and evolutionary extraction of delta-lognormal parameters: Performance comparison

Deterministic and evolutionary extraction of delta-lognormal...

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12th Conference of the International-Graphonomics-Society

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