Top PDF Order Reduction of Linear Interval Systems Using Genetic Algorithm

Order Reduction of Linear Interval Systems Using Genetic Algorithm

Order Reduction of Linear Interval Systems Using Genetic Algorithm

Abstract— This paper presents an algorithm for order reduction of higher order linear interval system into stable lower order linear interval system by means of Genetic algorithm. In this algorithm the numerator and denominator polynomials are determined by minimizing the Integral square error (ISE) using genetic algorithm (GA). The algorithm is simple, rugged and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. A numerical example illustrates the proposed algorithm.
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Using Genetic Algorithms for Building Metrics of Collaborative Systems

Using Genetic Algorithms for Building Metrics of Collaborative Systems

The paper objective is to reveal the importance of genetic algorithms in building robust metrics of collaborative systems. The main types of collaborative systems in economy are presented and some characteristics of genetic algorithms are described. A genetic algorithm was implemented in order to determine the local maximum and minimum points of the relative complexity function associated to a collaborative banking system. The intelligent collaborative systems based on genetic algorithms, representing the new generation of collaborative systems, are analyzed and the implementation of auto-adaptive interfaces in a banking application is described.
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Quasi-linear PCA: low order spline's approach to non-linear principal components

Quasi-linear PCA: low order spline's approach to non-linear principal components

Abstract—Nonlinear Principal Components Analysis (PCA) addresses the nonlinearity problem by relaxing the linear restrictions on standard PCA. A new approach on this subject is proposed in this paper, quasi-linear PCA. Basically, it recovers a spline based algorithm designed for categorical variables and introduces continuous variables into the framework without the need of a discretization process. By using low order spline transformations the algorithm is able to deal with nonlinear relationships between variables and report dimension reduction conclusions on the nonlinear transformed data as well as on the original data in a linear PCA fashion. The main advantages of this approach are; the user do not need to care about the discretization process; the relative distances within each vari- ables’ values are respected from the start without discretization losses of information; low order spline transformations allow recovering the relative distances and achieving piecewise PCA information on the original variables after optimization. An example applying our approach to real data is provided below.
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CONGESTION MANAGEMENT IN DEREGULATED POWER SYSTEMS USING REAL CODED GENETIC ALGORITHM

CONGESTION MANAGEMENT IN DEREGULATED POWER SYSTEMS USING REAL CODED GENETIC ALGORITHM

In this paper an attempt is made to explore the use of real coded genetic algorithm to find the optimal generation rescheduling for congestion management in deregulated environment. Congestion may occur in power system due to transmission line outages, generator outages, changes in energy demand and uncoordinated transactions. In this work, N-1 contingency analysis is carried out to identify the most severe lines and those lines are considered for analysis. The proposed problem is formulated as an optimization problem having large number of constraints. RCGA is used as optimization tool since it is an efficient heuristic algorithm for search and optimization. It operates on floating point representation of variables to be optimized. The method has been tested on IEEE 30 bus system successfully. In order to prove the effectiveness of the algorithm it is compared with SA. The simulation results proved that the RCGA yield economical solution than SA for all the test cases thereby enhances system security. Further more the line overloads are relieved completely by generation rescheduling alone without any load curtailment.
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Fast Optimization of Tuned Mass-Spring Arrangement for Reduction of Acoustic Power Radiation of Submerged Ribbed Cylinder

Fast Optimization of Tuned Mass-Spring Arrangement for Reduction of Acoustic Power Radiation of Submerged Ribbed Cylinder

To the authors’ best knowledge, the problem of “fully coupled vibroacoustic (e.g. acoustic power radiation) optimization of finite length submerged ribbed cylinders” has not been addressed in the literature yet. A submerged ribbed cylinder has been considered as a numerical test case (the model described by Zhou & Joseph (2005)) for coupled vibroacoustic FEM/BEM modeling. At first, two procedures have been proposed for computing BEM matrices in each frequency line. In order to solve governing equations, dimension reduction is inevitable, so the Krylov vectors (produced via Ritz or Arnoldi iterative procedures) and structural mode shapes (modal truncation approach) have been used and compared before performing optimization. The results showed good agreement with previous studies and experiments. Although Krylov reduction concept has been developed for simulating interior acoustic problems (cabin noise, etc.), using these basis vectors along optimization process of exterior vibroacoustic problems has not been reported in the literature. Finally, the optimum concentrated (point) mass and the dynamic absorber (TMD) arrangements were considered for the reduction of average radiated power using Genetic Algorithm. As expected, the majority of total point masses have been proposed by GA for the nearest regions to the excitation point but, this is not true for the case of dynamic absorbers (TMD). A noticeable point in the design of silent submerged vessels is that the most changes in this case proposed on the opposite side of the excitation point.
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Optimal dynamic control of laminated adaptive structures using a higher order model and a genetic algorithm

Optimal dynamic control of laminated adaptive structures using a higher order model and a genetic algorithm

Batra and Liang 11 used a three-dimensional linear theory of elasticity to find the optimal location of an actuator on a simple-supported rectangular laminated plate with embedded PZT layers. The optimal design is obtained by fixing the applied voltage and the size of the actuator and moving it around in order to find the maximum out-of-plane displacement. Liang et al. 12 proposed a model for the optimization of the induced-strain actuator location and configuration for active vibration control. Correia et al. 13 presented refined finite element models based on higher order displacement fields applied to the optimal design of laminated composite plates with embedded or surface bonded piezoelectric actuators and sensors.
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Encryption Image Using Small Order Linear Systems and Repeated Modular Numbers

Encryption Image Using Small Order Linear Systems and Repeated Modular Numbers

Form the testing of our algorithm which appearing in Table 1 we have a three encrypted images for each original image. Firstly we have obtained a plain image of an RGB color. Then this analysis by taking two sequential gray level numbers by constructing 2by2 linear system. Finally repeated modular numbers technique is used for production three matrices. Here it is interesting to note that the encrypted image do not has any resemblance with their corresponding original images. This fact ensures security of images in an effective manner. The histogram of each of original image and its decrypted image referee the goodness of this algorithm. The results of these histograms appear in Table 2 where ha and hb represent the histogram of original image and decrypted image respectively. The results appearing in Tables 1 and 2 are promised to develop.
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Feedback reduction of spatially multiplexed MIMO systems using compressive sensing

Feedback reduction of spatially multiplexed MIMO systems using compressive sensing

Abstract— In this paper we analyze spatially multiplexed MIMO systems with limited Channel State Information (CSI) and zero forcing (ZF) linear signal detection technique. Two schemes were considered: Quantization Codebook (QC) and Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in order to reduce feedback load into the system. Performance of the schemes are compared by computational simulations of bit error rate (BER) curves for the considered approaches QC and CS.

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A HYBRID GENETIC-LINEAR ALGORITHM FOR 2D INVERSION OF SETS OF VERTICAL ELECTRICAL SOUNDING

A HYBRID GENETIC-LINEAR ALGORITHM FOR 2D INVERSION OF SETS OF VERTICAL ELECTRICAL SOUNDING

LIU, P.; HARTZELL, S. A; STEPHENSON, W. Nonlinear multiparameter inversion using a hybrid global search algorithm: applications in reflec- tion seismology. Geophys. J. Int., [S.l.], v. 122, p. 991-1000, 1995. MCGILLIVRAY, P. R.; OLDENBURG, D. W. Methods forcalculating Fréchet derivatives and sensitivities for non-linear inverse problem: a compara- tive study. Geophys. Prosp., [S.l.], v. 38, p. 499-524, 1990. MEDEIROS, W. E. Eletro-resistividade aplicada à hidrogeologia do cristalino: um problema de modelamento bidimensional. 1987. Dissertação (Mestrado)-Universidade Federal da Bahia, Salvador, 1987. MEIJERINK, J. A.; VAN DER VORST, H. A. An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-ma- trix. Mathematics of Computation, [S.l.], v. 31, p. 148-162, 1977. MENKE, W. Geophysical data analysis: discrete inverse theory. New York: Academic Press, 1989.
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MIMO array capacity optimization using a genetic algorithm

MIMO array capacity optimization using a genetic algorithm

Abstract—This paper discusses the design of multiple- input–multiple-output (MIMO) antenna systems and proposes a genetic algorithm to obtain the position and orientation of each MIMO array antenna that maximizes the ergodic capacity for a given propagation scenario. One challenging task in the MIMO system design is to accommodate the multiple antennas in the mobile device without compromising the system capacity, due to spatial and electrical constraints. Based on an interface between the antenna model and the propagation channel model, the ergodic capacity is considered as the objective function of the MIMO array optimization. Simulation results corroborate the importance of polarization and antenna pattern diversities for MIMO in small terminals. Our results also show that the electromagnetic coupling effect can be exploited by the optimizer to decrease signal correlation and increase MIMO capacity. A comparison among a uniform linear array (ULA), a uniform circular array (UCA), and a genetic algorithm (GA)-optimized array is also carried out, showing that the topology given by the optimizer is superior to that of the standard ULA and UCA for the considered propagation channel.
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Peak Reduction in OFDM using Second Order Cone Programming Relaxation

Peak Reduction in OFDM using Second Order Cone Programming Relaxation

Convex optimization has recently emerged as an effi- cient tool for reducing the PAPR of OFDM signals [10-16]. This can be explained in part by the fact that con- vex optimization methods can efficiently compute global solutions to large-scale problems in polynomial time. In [15], an iterative second-order cone programming (SOCP) approach was proposed to pursue the quasi-constant PAPR value of OFDM signals. In [16], a semidefinite relax- ation (SDR) technique is employed to reduce the PAPR values of OFDM symbols. Although convex optimization approaches show advantages over the classical repeated clipping and filtering (RCF) approach [7], it is important to note that they may fail to deliver feasible solution to the PAPR problem; see [15,16]. This is due to the fact that in [15,16], the feasible (nonconvex) set of the original (non- convex) PAPR problem lies within the feasible (convex)
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Creating 3D object descriptors using a genetic algorithm

Creating 3D object descriptors using a genetic algorithm

The concept of optimization is associated to the determination of one or more possible solutions, which represent the extreme values of one or more fitness functions. Optimization techniques have a big relevance to the resolution of practical problems. When an optimization model represents one real system that involves just one function, it is called a mono objective problem. The problems that we are facing nowadays, in a technologically advanced world are complex. Sometimes the determination of optimal solutions may even be impossible and many times what we are looking for is just a good enough approximate solution. The main goal of this project is to obtain descriptors for 3D point clouds, using a genetic algorithm to obtain the parameters that will be used to build histograms to represent the point clouds. The descriptors will be tested in a large dataset of point clouds in order to evaluate their performance and compare them with other already existing descriptors.
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Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction

Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction

Recently rotational LDA technique [1] is presented which minimizes this limitation of LDA technique. In order to minimize the overlaps, it utilizes two transforms: rotational transform and orientation W. The rotational transform rotates the original feature space in such a way that thereafter the utilization of orientation W produces a reduced feature space which is most discriminative for different classes. The computation of is an iterative process which requires some components to be evaluated including the regions in the subspace belonging to classes. The boundaries of these regions are computed using minimum distance classification method. Therefore the boundaries of regions are dependent on the type of the classifier used. Thus the choice of classifier becomes crucial for separating regions in the reduced feature space which influences the overall classification performance. In this paper we have utilized Bayes decision theorem with Gaussian density function for this purpose. The utilization of Bayesian rule seems to improve the performance in terms of getting lesser classification error which is empirically demonstrated. Also an adaptive approach is adopted to compute within-class scatter matrix S W for the computation of orientation W.
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Neural Synchronization Using Genetic Algorithm for Secure Key Establishment

Neural Synchronization Using Genetic Algorithm for Secure Key Establishment

needed during the synchronization process. By doing so A and B can synchronize their neural networks without transmitting input values over the public channel. Of course, an opponent E does not know the secret seed state. Therefore E is unable to synchronize due to the lack of information about the input vectors. Even an active man-in-the-middle attack does not help in this situation, although it is always successful for public inputs. Consequently, reaching full synchronization proves that both participants know the secret seed state. Thus A and B can authenticate each other by performing this variant of the neural key exchange. The use of genetic algorithms can fasten the process of synchroniza-tion of genuine parties, so that many attacks, which are generally successful due to lengthy synchronization process, can be avoided. This makes the brute force attack very difficult. So in this paper we have discussed the Regular flipping Attack (RFA) and Majority Flipping Attack (MFA) strategy [9].
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Selection Of High Performing Geometry In Flexure Bearings For Linear Compressor Applications Using Fea.

Selection Of High Performing Geometry In Flexure Bearings For Linear Compressor Applications Using Fea.

[2] Saurabh Malpani, Yogesh Yenarkar, Dr. Suhas Deshmukh, S P Tak, D.V. Bhope ―Design OF Flexure Bearing For Linear Compressor by Optimization Procedure Using FEA‖, International Journal of Engineering Science and Technology (IJEST) Vol. 4 No.05 May 2012 pp.1991-1999

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A Heuristic Approach for optimization of Non Linear process using Firefly Algorithm and Bacterial Foraging Algorithm

A Heuristic Approach for optimization of Non Linear process using Firefly Algorithm and Bacterial Foraging Algorithm

Passino (2002) has proposed the Bacterial Foraging Algorithm based adaptive controller for a liquid level control problem. The perception of foraging activities of Escherichia coli (E. coli) bacteria is used for the optimization technique to find out the best fitted PID controller parameters by a set of artificial bacteria in the “D” dimensional search space. Many attempts by researchers have been carried out to find the optimal controller parameters using Bacterial Foraging Algorithm for different categories of engineering optimization problems . Datta et al. (2008) have proposed an improved adaptive approach involving Bacterial Foraging Algorithm to optimize both the amplitude and phase of the weights of a linear array of antennas for maximum array factor at any desired direction and nulls in specific directions[15]. In their work, it was found that Bacteria Foraging Algorithm is capable of improving the speed of convergence as well as the precision in the desired result. Bhushan et al. (2011) have been implemented the bacterial foraging algorithm for identification and high performance speed control system for a DC motor [16]. Recently, Rajinikanth and Latha (2012) discussed about the BFO-tuned I-PD controller performance on a class of time delayed unstable process models [17].
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Centralized approach for multi-node localization and identification

Centralized approach for multi-node localization and identification

The position of beacon in our algorithm at middle of the frame bottom edge has highlighted the important role of this position on the nodes visibility average. So, as compared with the robotic cluster matching algorithm, our algorithm shows an increase in the visibility average meaning that more accuracy in location estimation will be obtained. Also, our proposed algorithm shows a better performance than the robotic cluster matching algorithm in addressing the nodes under the effects of different parameters such as the rotating angle of beacon, nodes radius and the size of network.
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Rough Sets and Genetic Algorithm Using for Diagnostic Problems Solving

Rough Sets and Genetic Algorithm Using for Diagnostic Problems Solving

Model implementation was applied on real-world collaborative sets of data. It works better than comparable models. The present analysis and implementation of this approach provides the best results from another used standard methods. The importance of genetic algorithms using is in the following cases. In real-world situations we often have to solve the hard optimization problems that are usually not possible to solve by classical approaches. In practice, we usually have:

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An Automated Algorithm for Approximation of Temporal Video Data Using Linear B'EZIER Fitting

An Automated Algorithm for Approximation of Temporal Video Data Using Linear B'EZIER Fitting

Reasons to choose linear Bezier: (1) Due to large size of video data simple and efficient fitting is inevitable. Linear Bezier is most efficient and simple than other types curves e.g., quadratic/cubic Bezier curves, Natural cubic spline, B-spline, Cardinal spline etc. There is no continuity constraint in the adjacent segments of Bezier curves i.e., each linear Bezier segment can be constructed independently of other segments and abrupt changes in pixel intensity can be approximated by multiple linear Bezier segments efficiently. (2) There is no middle control point in linear Bezier unlike quadratic/cubic Bezier curves, further end-control points are in the same data range, as original data, i.e., [0-255]. Therefore, linear Bezier needs less data (low entropy) to save and is suitable for video data compression. (3) Fitting of data by linear Bezier is local whereas fitting of data using other types of splines e.g., Natural cubic spline or Cardinal spline is non-local. Local fitting means breaking of a segment into two segments in Bezier fitting requires only re-computation of two newly created segments without affecting the remaining segments. Therefore whenever a fitted segment splits due to large change in pixel intensity, the computation cost remains within acceptable limit. The locality of Bezier fitting is at the expense of less continuity. Linear Bezier is C 0 continuous, this result in lack of smoothness. But this lesser continuity does not impact the quality of reconstructed video because it depends on closeness of fit to original data rather than smoothness of fit. Smoothness of fit is desirable feature in applications like font design where smooth curves are more pleasing.
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Optimization of technical trading rules in forex market using genetic algorithm

Optimization of technical trading rules in forex market using genetic algorithm

During the in-the-sample period there are two strong trends: a bullish market until the middle of 2008 – despite financial crisis, subprime crisis, has begun in the summer of 2007, the bullish trend in foreign exchange market only reversed to a bearish trend in the summer of 2008 - and therefore a strong bearish trend took control of the market until the end of the in-the-sample period, end of 2009. Throughout the out-of-sample period the market has continued to decrease, however it has found some resistance levels where the market has been moving/moved sideways. In these sideways zones, the market volatility has increased comparing with the trend periods, which creates a good sample to test the best indicator optimized in the out-of-sample period, because it is tested in a different environment. It is important to reference that on September 6, 2011, the Suisse National Bank set a minimum exchange rate of 1.20 francs per euro, which has decreased its volatility since then.
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