Search results for: MRAS algorithm.

Authors: Mohammad Jafarifar

Abstract:

This paper presents a speed estimation scheme based on second-order sliding-mode Super Twisting Algorithm (STA) and Model Reference Adaptive System (MRAS) estimation theory for Sensorless control of multiphase induction machine. A stator current observer is designed based on the STA, which is utilized to take the place of the reference voltage model of the standard MRAS algorithm. The observer is insensitive to the variation of rotor resistance and magnetizing inductance when the states arrive at the sliding mode. Derivatives of rotor flux are obtained and designed as the state of MRAS, thus eliminating the integration. Compared with the first-order sliding-mode speed estimator, the proposed scheme makes full use of the auxiliary sliding-mode surface, thus alleviating the chattering behavior without increasing the complexity. Simulation results show the robustness and effectiveness of the proposed scheme.

Keywords: Multiphase induction machine, field oriented control, sliding mode, super twisting algorithm, MRAS algorithm.

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Authors: A.Venkadesan, S.Himavathi, A.Muthuramalingam

Abstract:

Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most popularly used conventional speed estimation scheme for sensor-less IM drives. In this scheme, the voltage model equations are used for the reference model. This encounters major drawbacks at low frequencies/speed which leads to the poor performance of RF-MRAS. Replacing the reference model using Neural Network (NN) based flux estimator provides an alternate solution and addresses such drawbacks. This paper identifies an NN based flux estimator using Single Neuron Cascaded (SNC) Architecture. The proposed SNC-NN model replaces the conventional voltage model in RF-MRAS to form a novel MRAS scheme named as SNC-NN-MRAS. Through simulation the proposed SNC-NN-MRAS is shown to be promising in terms of all major issues and robustness to parameter variation. The suitability of the proposed SNC-NN-MRAS based speed estimator and its advantages over RF-MRAS for sensor-less induction motor drives is comprehensively presented through extensive simulations.

Keywords: Sensor-less operation, vector-controlled IM drives, SNC-NN-MRAS, single neuron cascaded architecture, RF-MRAS, artificial neural network

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Authors: Youcef Bekakra, Djilani Ben attous

Abstract:

In this paper, a field oriented control (FOC) induction motor drive is presented. In order to eliminate the speed sensor, an adaptation algorithm for tuning the rotor speed is proposed. Based on the Model Reference Adaptive System (MRAS) scheme, the rotor speed is tuned to obtain an exact FOC induction motor drive. The reference and adjustable models, developed in stationary stator reference frame, are used in the MRAS scheme to estimate induction rotor speed from measured terminal voltages and currents. The Integral Proportional (IP) gains speed controller are tuned by a modern approach that is the Particle Swarm Optimization (PSO) algorithm in order to optimize the parameters of the IP controller. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. The proposed algorithm has been tested by numerical simulation, showing the capability of driving load.

Keywords: Induction motor drive, field oriented control, model reference adaptive system (MRAS), particle swarm optimization (PSO).

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Authors: A. Ameur, B. Mokhtari, N. Essounbouli, L. Mokrani

Abstract:

This paper presents a speed sensorless direct torque control scheme using space vector modulation (DTC-SVM) for permanent magnet synchronous motor (PMSM) drive based a Model Reference Adaptive System (MRAS) algorithm and stator resistance estimator. The MRAS is utilized to estimate speed and stator resistance and compensate the effects of parameter variation on stator resistance, which makes flux and torque estimation more accurate and insensitive to parameter variation. In other hand the use of SVM method reduces the torque ripple while achieving a good dynamic response. Simulation results are presented and show the effectiveness of the proposed method.

Keywords: MRAS, PMSM, SVM, DTC, Speed and Resistance estimation, Sensorless drive

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Authors: Nadia Bensiali, Nadia Benalia, Amar Omeiri

Abstract:

The recent trend in field oriented control (FOC) is towards the use of sensorless techniques that avoid the use of speed sensor and flux sensor. Sensors are replaced by estimators or observers to minimise the cost and increase the reliability. In this paper an anlyse of perfomance of a MRAS used in sensorless control of induction motors and sensitvity to machine parameters change are studied.

Keywords: Induction motor drive, adaptive observer, MRAS, stability analysis.

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Authors: Grouz Faten, Sbita Lassaâd

Abstract:

In this paper, Speed Sensorless Indirect Field Oriented Control (IFOC) of a Permanent Magnet Synchronous machine (PMSM) is studied. The closed loop scheme of the drive system utilizes fuzzy speed and current controllers. Due to the well known drawbacks of the speed sensor, an algorithm is proposed in this paper to eliminate it. In fact, based on the model of the PMSM, the stator currents and rotor speed are estimated simultaneously using adaptive Luenberger observer for currents and MRAS (Model Reference Adaptive System) observer for rotor speed. To overcome the sensivity of this algorithm against parameter variation, adaptive for on line stator resistance tuning is proposed. The validity of the proposed method is verified by an extensive simulation work.

Keywords: PMSM, Indirect Field Oriented Control, fuzzy speed and currents controllers, Adaptive Luenberger observer, MRAS.

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Authors: Hicham Serhoud, Djilani Benattous

Abstract:

In this paper present a sensorless maximum wind power extraction for variable speed constant frequency (VSCF) wind power generation systems with a doubly-fed induction generators (DFIG), to ensure stability and to impose the ideal feedback control solution despite of model uncertainties , using the principles of an active and reactive power controller (DPC) a robust sliding mode power control has been proposed to guarantees fast response times and precise control actions for control the active and reactive power independently. The simulation results in MATLAB/Simulink platform confirmed the good dynamic performance of power control approach for DFIGbased variable speed wind turbines.

Keywords: Doubly fed induction generator , sliding modecontrol, maximal wind energy capture, MRAS estimator

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Authors: Gaohuizi Guo, Ning Zhang

Abstract:

Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA.

Keywords: Firefly algorithm, hybrid algorithm, multi-objective optimization, Sine Cosine algorithm.

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Authors: Safeer Hussain Khan

Abstract:

In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms.

Keywords: Contractive-like operator, iterative algorithm, fixed point, strong convergence.

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Authors: Satoshi Shimizu, Kazuaki Yamaguchi, Toshiki Saitoh, Sumio Masuda

Abstract:

Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.

Keywords: Maximum weight clique, exact algorithm, branch-andbound, NP-hard.

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Authors: Panpan Xu, Shulin Sui

Abstract:

The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

Keywords: Genetic algorithm, Adaptive genetic algorithm, Function optimization.

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Authors: Mir Hadi Seyedafsari, Iraj Hasanzadeh

Abstract:

Like other external sorting algorithms, the presented algorithm is a two step algorithm including internal and external steps. The first part of the algorithm is like the other similar algorithms but second part of that is including a new easy implementing method which has reduced the vast number of inputoutput operations saliently. As decreasing processor operating time does not have any effect on main algorithm speed, any improvement in it should be done through decreasing the number of input-output operations. This paper propose an easy algorithm for choose the correct record location of the final list. This decreases the time complexity and makes the algorithm faster.

Keywords: External sorting algorithm, internal sortingalgorithm, fast sorting, robust algorithm.

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Authors: Amod P. Vaze

Abstract:

Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.

Keywords: Genetic algorithm, analog circuits, design.

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Authors: Panpan Xu, Shulin Sui, Zongjie Du

Abstract:

Genetic algorithm is widely used in optimization problems for its excellent global search capabilities and highly parallel processing capabilities; but, it converges prematurely and has a poor local optimization capability in actual operation. Simulated annealing algorithm can avoid the search process falling into local optimum. A hybrid genetic algorithm based on simulated annealing is designed by combining the advantages of genetic algorithm and simulated annealing algorithm. The numerical experiment represents the hybrid genetic algorithm can be applied to solve the function optimization problems efficiently.

Keywords: Genetic algorithm, Simulated annealing, Hybrid genetic algorithm, Function optimization.

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Authors: Chenxue Yang, Mao Ye, Zijian Liu, Tao Li, Jiao Bao

Abstract:

Non-negative matrix factorization (NMF) is a useful computational method to find basis information of multivariate nonnegative data. A popular approach to solve the NMF problem is the multiplicative update (MU) algorithm. But, it has some defects. So the columnwisely alternating gradient (cAG) algorithm was proposed. In this paper, we analyze convergence of the cAG algorithm and show advantages over the MU algorithm. The stability of the equilibrium point is used to prove the convergence of the cAG algorithm. A classic model is used to obtain the equilibrium point and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the cAG algorithm are obtained, which help reducing the evaluation time and is confirmed in the experiments. By using the same method, the MU algorithm has zero divisor and is convergent at zero has been verified. In addition, the convergence conditions of the MU algorithm at zero are similar to that of the cAG algorithm at non-zero. However, it is meaningless to discuss the convergence at zero, which is not always the result that we want for NMF. Thus, we theoretically illustrate the advantages of the cAG algorithm.

Keywords: Non-negative matrix factorizations, convergence, cAG algorithm, equilibrium point, stability.

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Authors: S. M. Khalessizadeh, R. Zaefarian, S.H. Nasseri, E. Ardil

Abstract:

Today, Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. This researches show a better performance due to the nature of Genetic Algorithm. In this paper a new algorithm for using Genetic Algorithm in concept weighting and topic identification, based on concept standard deviation will be explored.

Keywords: Genetic Algorithm, Text Mining, Term Weighting, Concept Extraction, Concept Distribution.

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Authors: Keonwoo Kim, Dowon Hong, Kyoil Chung

Abstract:

This paper proposes the authentication method using ESA algorithm instead of using CAVE algorithm in the CDMA mobile communication systems including IS-95 and CDMA2000 1x. And, we analyze to apply ESA mechanism on behalf of CAVE mechanism without the change of message format and air interface in the existing CDMA systems. If ESA algorithm can be used as the substitution of CAVE algorithm, security strength of authentication algorithm is intensified without protocol change. An algorithm replacement proposed in this paper is not to change an authentication mechanism, but to configure input of ESA algorithm and to produce output. Therefore, our proposal can be the compatible to the existing systems.

Keywords: ESA, CAVE, CDMA, authentication, mobilecommunication.

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Authors: Razagh Hafezi, Ahmad Keshavarz, Vida Moshfegh

Abstract:

This paper presents a novel algorithm of stereo correspondence with rank transform. In this algorithm we used the genetic algorithm to achieve the accurate disparity map. Genetic algorithms are efficient search methods based on principles of population genetic, i.e. mating, chromosome crossover, gene mutation, and natural selection. Finally morphology is employed to remove the errors and discontinuities.

Keywords: genetic algorithm, morphology, rank transform, stereo correspondence

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Authors: Ali Nourollah, Mohsen Movahedinejad

Abstract:

In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth.

Keywords: Divide and conquer, genetic algorithm, merge polygons, Random simple polygon generation.

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Authors: Luciano Nieddu, Giuseppe Manfredi

Abstract:

In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores.

Keywords: K-means, Industrial ores classification, Invariant Features, Supervised Classification.

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Authors: M. A. Sharbafi, M. Shakiba Herfeh, Caro Lucas, A. Mohammadi Nejad

Abstract:

Several researchers have proposed methods about combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of GA to obtain fuzzy rules and application of fuzzy logic in optimization of GA). In this paper, we suggest a new method in which fuzzy decision making is used to improve the performance of genetic algorithm. In the suggested method, we determine the alleles that enhance the fitness of chromosomes and try to insert them to the next generation. In this algorithm we try to present an innovative vaccination in the process of reproduction in genetic algorithm, with considering the trade off between exploration and exploitation.

Keywords: Genetic Algorithm, Fuzzy Decision Making.

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Authors: Branislav Vuksanović, Dragana Nikolić

Abstract:

This paper describes the implementation and testing of a multichannel active noise control system (ANCS) based on the filtered-inverse LMS (FILMS) algorithm. The FILMS algorithm is derived from the well-known filtered-x LMS (FXLMS) algorithm with the aim to improve the rate of convergence of the multichannel FXLMS algorithm and to reduce its computational load. Laboratory setup and techniques used to implement this system efficiently are described in this paper. Experiments performed in order to test the performance of the FILMS algorithm are discussed and the obtained results presented.

Keywords: Active noise control, adaptive filters, inverse filters, LMS algorithm, FILMS algorithm.

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Authors: Mahmud Hesain ALdwaik, Omar Hsiain Eldwaik

Abstract:

This paper deals with tracking and estimating time delay between two signals. The simulation of this algorithm accomplished by using Mathcad package is carried out. The algorithm we will present adaptively controls and tracking the delay, so as to minimize the mean square of this error. Thus the algorithm in this case has task not only of seeking the minimum point of error but also of tracking the change of position, leading to a significant improving of performance. The flowchart of the algorithm is presented as well as several tests of different cases are carried out.

Keywords: Tracking time delay, Algorithm simulation, Mathcad, MSE

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Authors: Moth'd Belal. Al-Daoud

Abstract:

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.

Keywords: clustering, k-means, data mining.

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Authors: Vishnu Pratap Singh Kirar

Abstract:

Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a twoterm algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: Neural Network, Backpropagation, Local Minima, Fast Convergence Rate.

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Authors: B.Bahmani Firouzi, T. Niknam, M. Nayeripour

Abstract:

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.

Keywords: Data clustering, Hybrid evolutionary optimization algorithm, K-means algorithm, Simulated Annealing (SA), Particle Swarm Optimization (PSO).

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Authors: B. Arun Kumar, T. Ravichandran

Abstract:

Workflow scheduling is an important part of cloud computing and based on different criteria it decides cost, execution time, and performances. A cloud workflow system is a platform service facilitating automation of distributed applications based on new cloud infrastructure. An aspect which differentiates cloud workflow system from others is market-oriented business model, an innovation which challenges conventional workflow scheduling strategies. Time and Cost optimization algorithm for scheduling Hybrid Clouds (TCHC) algorithm decides which resource should be chartered from public providers is combined with a new De-De algorithm considering that every instance of single and multiple workflows work without deadlocks. To offset this, two new concepts - De-De Dodging Algorithm and Priority Based Decisive Algorithm - combine with conventional deadlock avoidance issues by proposing one algorithm that maximizes active (not just allocated) resource use and reduces Makespan.

Keywords: Workflow Scheduling, cloud workflow, TCHC algorithm, De-De Dodging Algorithm, Priority Based Decisive Algorithm (PBD), Makespan.

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Authors: Xiangbin Zhu

Abstract:

EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.

Keywords: Fuzzy control, real-time systems, EDF, misdeadline ratio.

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Authors: Safa Adi

Abstract:

This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences.

Keywords: Database, GTC algorithm, PSP algorithm, sequential patterns, time constraints.

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Authors: Wullapa Wongsinlatam

Abstract:

Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: Artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization.

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