Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK®

Sumathi, S.; Kumar, L. Ashok; Surekha, P.

doi:10.1201/b19134

Cited by 46 publications

(62 citation statements)

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“…Since it is impossible to recognise the behaviour of the data correctly, a combined approach, based on both A typical neural network comprises three primary layers, including input, intermediate, and output layers. Based on the basic concepts of machine learning, the number of hidden (intermediate) layers does not have a theoretical limit [50]. A typical ANN structure is depicted in Figure 2.…”

Section: The Hybrid Methodologymentioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

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Abstract:Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.

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Section: The Hybrid Methodologymentioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

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“…Complexity and nonlinear interdependences of observed optimization problem cause difficulty in providing the fast and correct solution using classical (exact) optimization techniques such as linear programming, interior point method or mixed integer programming [3].…”

Section: Artificial Neural Network Design and Implementationmentioning

confidence: 99%

“…Learning process, a variant of the Delta Rule [3], is next step in ANN design since it evaluates the efficacy of ANN creation. Learning starts with determining the relationships between layers and neurons by error assessment.…”

Section: Artificial Neural Network Design and Implementationmentioning

confidence: 99%

“…ANN learning and adaptation characteristic is recognized to have a great potential in control systems because it gives them the opportunity to approximate nonlinear functions, be suited for parallel and distributed processing and model multivariable systems naturally [3]. Since ANN is based on human experience and on functional links between input and output variables, they can be used in various learning mechanisms and selforganization concepts, pattern recognition, trend determination, forecasting, function fitting etc.…”

Section: Introductionmentioning

confidence: 99%

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Voltage and power losses control using distributed generation and computational intelligence

2016

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Original scientific paper The paper analyzes the possibility of reducing active power losses in power system, constrained by regulated voltage levels, by implementing appropriate distributed generation capacity. The objectives of this paper were achieved by developing hybrid methods based on artificial neural network and genetic algorithm. Methods have been developed to determine the impact of different distributed generation power on all terminals in the observed system. The method that uses artificial neural network and genetic algorithm is applicable for radial distribution networks, and method using load flow and genetic algorithm is applicable to doubly-fed distribution network. For comparison purposes, additional method was developed that uses neural networks for the decision-making process. Data for training the neural network was obtained by power flow calculation in the DIgSILENT PowerFactory software on a part of Croatian distribution network. The same software was used as an analytical tool for checking the correctness of solutions obtained by optimization. Keywords: artificial neural networks; distributed generation; genetic algorithm Kontrola naponskih prilika i gubitaka snage korištenjem distribuirane proizvodnje i računalne inteligencijeIzvorni znanstveni članak U radu se analizira mogućnost smanjenja aktivnih gubitaka elektroenergetskog sustava, uz poštivanje propisanih naponskih razina, primjenom odgovarajućih kapaciteta distribuirane proizvodnje. Ciljevi ovog rada ostvareni su razvojem hibridnih metoda baziranih na umjetnim neuronskim mrežama i genetskom algoritmu. Razvijene su metode za određivanje utjecaja distribuirane proizvodnje različitih snaga na svim čvorovima u promatranom sustavu. Metoda koja koristi umjetnu neuronsku mrežu i genetski algoritam primjenjiva je za radijalne distributivne mreže, a metoda koja koristi proračun tokova snaga i genetski algoritam primjenjiva je za dvostruko napajane distributivne mreže. S ciljem usporedbe razvijena je i metoda koja koristi neuronske mreže za proces odlučivanja o najboljem rješenju. Podaci za učenje neuronske mreže dobiveni su proračunom tokova snaga u programskom alatu DIgSILENT PowerFactory i to na djelu hrvatske distributivne mreže. Isti programski alat se koristi analitički kao sredstvo provjere ispravnosti rješenja dobivenih optimizacijom.

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“…3. In addition, the results of the AQI of the fused image and the normalized mean square error (NMSE) 30 between the original image and the merged image are shown in Table 2.…”

Section: Simulated Datamentioning

confidence: 99%

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

et al. 2018

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, "Focus measure method based on the modulus of the gradient of the color planes for digital microscopy," Opt. Eng. 57(2), 023106 (2018), doi: 10.1117/1.OE.57.2.023106. Abstract. The modulus of the gradient of the color planes (MGC) is implemented to transform multichannel information to a grayscale image. This digital technique is used in two applications: (a) focus measurements during autofocusing (AF) process and (b) extending the depth of field (EDoF) by means of multifocus image fusion. In the first case, the MGC procedure is based on an edge detection technique and is implemented in over 15 focus metrics that are typically handled in digital microscopy. The MGC approach is tested on color images of histological sections for the selection of in-focus images. An appealing attribute of all the AF metrics working in the MGC space is their monotonic behavior even up to a magnification of 100×. An advantage of the MGC method is its computational simplicity and inherent parallelism. In the second application, a multifocus image fusion algorithm based on the MGC approach has been implemented on graphics processing units (GPUs). The resulting fused images are evaluated using a nonreference image quality metric. The proposed fusion method reveals a high-quality image independently of faulty illumination during the image acquisition. Finally, the three-dimensional visualization of the in-focus image is shown. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK®

Cited by 46 publications

References 0 publications

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

Voltage and power losses control using distributed generation and computational intelligence

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

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