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The aim of the present study was to investigate the role of ADAMTS-7 gene in the pathogenesis of myocardial dystrophy associated with white muscle disease (WMD) in lambs. A total of 217 cardiac tissue samples from lambs with WMD were used in the study. Histopathological sections of the samples were stained with hematoxylin-eosin (HE) and examined using Western- blot, real-time PCR (RT-PCR) and immunohistochemistry for ADAMTS-7 gene expression, and the findings were statistically evaluated. Histopathological examinations revealed fibrosis associated with hyalinization, necrosis and granular calcifications in cardiomyocytes. Western blot and RT-PCR showed a statistically significant upregulation of ADAMTS-7 (p

Kucukaslan, Ibrahim/0000-0002-3458-4409; WOS: 000279231200003 Although cytopathology is widely used for early diagnosis of human tumours, it is not commonly performed in veterinary medicine. The aim of the present study was to compare cytological examination after pre-operative fine needle aspiration biopsies from canine mammary tumours (n = 31) with classical histopathology performed after surgery. Among the 26 available aspirates from various and heterogeneous mammary gland tumour masses, 20 exhibited atypical epithelial cells coupled to nucleus and/or chromatin anomalies, mitotic figures or spindle shape cells and were classified as malignant, 3 only showed modified epithelial cells and were considered as malignant suspected and 3 aspirates were considered as benign because epithelial cells appeared uniform. The most frequent histological types of tumours were malignant mixed tumours and adenocarcinomas, mainly tubular and papillary adenocarcinomas. The agreement score between the 2 techniques was 88.5%, the cytologically suspected malignant tumours being malignant by histology. These results suggest that pre-operative cytopathological examination of mammary masses may be helpful in the early malignancy diagnosis and in the therapeutic decision. Ankara UniversityAnkara University [2006 0810083] This study was supported by Management of Scientific Research Projects of the Ankara University (Project number: 2006 0810083).

International Multiconference of Engineers and Computer Scientists -- MAR 19-21, 2008 -- -- Hong Kong, PEOPLES R CHINA, WOS: 000256665701061, The Newton-Raphson method is one of the most widely used methods for minimization. It can be easily generalized for solving non-linear differential equation systems. In this study, Generalized Predictive Controller (GPC) was applied to a 6R robot manipulator based on joint control. Newton-Raphson (N-R) method was used to minimize the cost function existing in the GPC that represents errors between reference trajectory and actual trajectory in the control of robot. The Newton-Raphson method requires less iteration numbers for convergence and reduces the calculation. This study presents a detailed derivation of the Generalized Predictive Control algorithm with Newton-Raphson minimization method. The results of angular path and position errors belonging to joints were examined and compared with Recursive Least Square (RLS) implemented Generalized Predictive Control. The simulation results showed that Newton-Raphson method improved control performance of the GPC. http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1347-1352.pdf, Int Assoc Engineers, Int Assoc Engn, Soc Artificial Intelligence, Int Assoc Engn, Soc Bioinformat, Int Assoc Engn, Soc Comp Sci, Int Assoc Engn, Soc Data Mining, Int Assoc Engn, Soc Elect Engn, Int Assoc Engn, Soc Imaging Engn, Int Assoc Engn, Soc Info Syst Eng, Int Assoc Engn, Soc Internet Comp & Web Serv, Int Assoc Engn, Soc Mech Engn, Int Assoc Engn, Operat Res, Int Assoc Engn, Sci Comp, Int Assoc Engn, Soc Software Engn, Int Assoc Engn, Soc Wireless Networks, scientific research fund (University of Sakarya) [2006.50.02.050], This work was supported by scientific research fund (University of Sakarya, grant no. 2006.50.02.050).

Bu çalismada, Genellestirilmis Öngörülü Kontrol (GPC – Generalized Predictive Control) ve Newton-Raphson Uyarlamali Yapay Sinir Agli Genellestirilmis Öngörülü Kontrol (NGPC – Neural Generalized Predictive Control) algoritmalari incelenmis olup her biri Tek Giris Tek Çikis (SISO – Single Input Single Output) ve Çok Giris Çok Çikis (MIMO – Multiple Inputs Multiple Outputs) olmak üzere iki sekilde alti eklemli bir robot koluna eklem esasli yörünge kontrolü için uygulanmistir. Robot kolunun dinamik olarak modellenmesinde Lagrange-Euler yöntemi kullanilmistir. Dinamik modellemeye sürtünme etkileri, yük tasima ve tasinan yükün tasima esnasinda düsmesi durumlari da ayrica ilave edilmistir. Elde edilen dinamik model, 4. mertebeden Runge-Kutta bütünlestirme yöntemi kullanilarak robot kolu simülatörüne dönüstürülmüstür. Robot kolu eklemlerinin yörünge takibi kübik ve sinüzoidal yörünge esaslarina göre belirlenmistir. Kontrol algoritmalari farkli örnek ve durumlar için kendi aralarinda kiyaslanmistir. Gerekli bütün yazilimlar tek bir paket program halinde Borland Delphi 6.0 programlama dili kullanilarak gerçeklestirilmistir., In this thesis study, GPC (Generalized Predictive Control) and Newton-Raphson implemented NGPC (Neural Generalized Predictive Control) algorithms belong to the class of MBPC (Model Based Predictive Control) are investigated and each of them is applied to a six joint robotic arm as SISO (Single Input Single Output) and MIMO (Multiple Inputs Multiple Outputs) for the joint based trajectory control. Dynamics modeling of the robotic arm is made by using the Lagrange-Euler equations. The frictional effects, the state of carrying and falling load are added to dynamics model. Dynamics model obtained is transformed into robotic arm simulator by using 4th degree Runge-Kutta integration method. The trajectory planning for the joints of the robotic arm is designated according to the cubic and sinusoidal trajectories principles. The control algorithms are compared with themselves for different examples and cases. The simulation program included all of these is prepared by using Borland Delphi 6.0 programming language.

17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems -- MAY 17-20, 2004 -- -- Ottawa, CANADA, WOS: 000221714200107, In this study, the method to apply the Elman's recurrent neural networks for harmonic detection process in active filter is proposed. The feed forward neural networks were also used for comparison. We simulated the distorted wave including 5(th), 7(th), 11(th), 13(th) harmonics and used them for training of the neural networks. The distorted wave including up to 25(th) harmonics were prepared for testing of the neural networks. Elman's recurrent and feed forward neural networks were used to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively.

Air Force Office of Scientific Research;Asian Office of Aerospace Research and Development, Japan;Auckland University of Technology, New Zealand, 8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence -- 9 August 2004 through 13 August 2004 -- Auckland -- 65297, In this study, the method to apply the Elman's recurrent neural networks using resilient back propagation for harmonic detection is described. The feed forward neural networks are also used for comparison. The distorted wave including 5th, 7th, 11th, 13th harmonics were simulated and used for training of the neural networks. The distorted wave including up to 25th harmonics were prepared for testing of the neural networks. Elman's recurrent and feed forward neural networks were used to recognize each harmonic. The results obtained using Elman's recurrent neural networks are better than the results values obtained using the feed forward neural networks for resilient back propagation. © Springer-Verlag Berlin Heidelberg 2004.

Some solvents in commercial products may have harmful effects on human health. It is important to determine the percentage of this certain solvent in a product to detect any possible health hazards. In this paper, three different solvents, acetone, methanol, and chloroform, are used to form binary gas mixtures in a laboratory environment. Nine quartz-crystal microbalance sensors are used, and gas data are obtained through the responses of these sensors. First, the data set divided 11 times randomly for validation sensitivity of the results. For each of the binary gas mixtures, insignificant sensors are removed, considering multivariate analysis of variance analysis, and sensor data sets are obtained. The statistical multivariate linear regression (MvLR) method is used to determine the ratio of individual gasses in each binary gas mixture. Flexible models are created by removing insignificant sensor data from the equations in the MvLR. Prediction performances of 11 data sets reveal and validate that statistical methods can be used to detect the ratio of a certain gas within a gas mixture, and reliable results can be achieved.

Natural gas is the most basic energy source used today in energy production, heating and cooking. With its widespread network, houses, industrial enterprises and power plants can access this energy at any time. The natural gas used in Turkey is an imported energy source and its agreements is provided by long-term contracts. Long-term contracts are submitted to the domestic market by decision-makers. In this process, natural gas supply companies and wholesale companies, provide gas supplies to city distribution companies and industrial establishments with annual contracts (mid-term). City distribution or other companies are required to report monthly and year based daily consumption demand forecasts in these contracts. This paper studies forecasting of daily and monthly demand for mid-term natural gas as contract estimations using time series decomposition, Holt-Winters and ARIMA / SARIMA models, which are statistical methods, include univariate seasonality. In the study, 365-day forecast is performed on a daily basis and 12-month forecast is performed on a monthly basis at once. As a result of daily natural gas estimation, the lowest error is realized by ARIMA(0,0,1)1(0,1,1)365 model with 23.68% MAPE in the year ahead prediction. In the monthly conversion, the lowest estimation model is realized by ARIMA(1,0,1)1(1,1,1)365 model with 11.84% MAPE. The results show that seasonal ARIMA models are the most suitable among the univariate techniques. The fact that many predictions can be made at a time and the results are acceptable allow these techniques to be used by decision-makers., Doğal gaz günümüzde enerji üretimi, ısınma ve pişirmede kullanılan en temel enerji kaynağıdır. Yaygın ağ yapısı ile birlikte evler, sanayi kuruluşları, santraller istedikleri anlarda bu enerjiye erişebilmektedir. Türkiye’de doğal gaz ithal bir enerji kaynağıdır ve uzun dönemli sözleşmeler ile anlaşmalar sağlanmaktadır. Uzun dönemli sözleşmeler karar vericiler tarafından yurtiçine arz edilir. Bu arz sürecinde doğal gaz tedarik şirketleri ve toptan satış şirketleri şehir dağıtım şirketleri ve sanayi kuruluşlarına yıllık sözleşmeler ile gaz arzı sağlarlar. Şirketler ve şehir dağıtım şirketleri bu sözleşmelerde aylık, yıl içinde de günlük tüketim talep tahminlerini bildirmekle yükümlüdür. Bu çalışma günlük ve aylık temelde orta vadeli doğal gaz talep tahminini tek değişkenli mevsimsellik içeren istatistiki yöntemler olan zaman serileri ayrıştırılması, Holt-Winters ve ARIMA/SARIMA modelleri ile gerçekleştirmiştir. Yapılan çalışmada günlük temelde 365 günlük, aylık temelde de 12 aylık tahmin bir anda gerçekleştirilmiştir. Doğal gaz tahmini sonucu günlük temelde en düşük hata yıl öncesi tahminde ARIMA(0,0,1)1(0,1,1)365 modeli ile 23,68% MAPE ile gerçekleşmiştir. Aylık dönüşümde ise en düşük tahmin modeli ARIMA(1,0,1)1(1,1,1)365 modeli ile 11,84% MAPE ile gerçekleşmiştir. Bu sonuçlar mevsimsel ARIMA modellerinin tek değişkenli teknikler arasında en uygun olduğunu göstermiştir. Bir anda çok sayıda tahmin yapılabilmesine imkan tanıması ve sonuçlarının kabul edilebilir olması bu tekniklerin karar vericiler tarafından kullanılabilmesine olanak tanımaktadır.

System identification is an important process to investigate and understand the behavior of an unknown system. It aims to establish an interface between the real system and its mathematical representation. Conventional system identification methods generally need differentiable search spaces and they cannot be used for nondifferentiable multimodal search spaces. On the other hand, metaheuristic search algorithms are independent from the search space characteristics and they do not need much knowledge about the real system. The migrating birds optimization algorithm is a recently introduced nature-inspired metaheuristic neighborhood search approach. It simulates the V flight formation of migrating birds, which enables birds to save energy during migration. In this paper, first, a set of comparative performance tests by using benchmark functions are performed on the migrating birds optimization algorithm and some other well-known metaheuristics. The same metaheuristic algorithms are then employed to solve several system identification problems. The results show that the migrating birds optimization algorithm achieves promising optimizations both for benchmark tests and for system identification problems.

The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC). For this purpose, households and low-consuming commercial users' four-year consumption data between the years of 2011-2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE) for training utilizing MAPE with a univariate sliding window technique.

Household consumption has a significant importance for natural gas wholesale companies. These companies make one-day-ahead forecasting daily. However, there are penalties depending on the error of the estimates. These penalties increase exponentially depending on the error rate. Several studies have been done to develop mathematical models to forecast natural gas consumption and minimize the error rate. However, before mathematical model predictions, a previous step, data preparation, is also important. The data must be prepared correctly before the mathematical model. At this point, prior to the mathematical model, selecting the appropriate data set size has a vital role. In this study, one-day-ahead household natural gas consumption is forecasted for different data sizes. Forecasts have been made for the year 2012. For removing insignificant variables, multiple linear regression (MLR) is applied to all data. In this research, 2 particular scenarios are applied for forecasting. In the first scenario, 2 different data set models are prepared. These sets consist of the data collected 6 weeks before the forecasted day. Daily outcomes are added to the data set and the set is applied in a model called Model A. The other model is depicted based on a sliding window idea having 6 weeks of fixed data size with dynamic data inside (Model W6). For the two models, MLR is applied and error rates are compared. Here, Model A has 7 times higher mean absolute percent error (MAPE) than Model W6. In scenario 2, 6 models are studied and compared for the sliding window approach. The models are named according to the weeks involved (e.g., Model W1, Model W6). MAPEs for Model W3, Model W4, Model W5, and Model W6 are obtained as 11.8%, 6.8%, 7.2%, and 8.1%, respectively. The lowest preday error occurs in the 4-week data model with sliding window approach.

Transformer design optimization methods presented in the literature rarely yield solutions directly applicable in production; the design engineer usually needs to convert the theoretical solution to a practical one. This problem is addressed in this paper, and a discrete transformer design optimization method is proposed that yields solutions with commercially available or productionally feasible dimensions, thus eliminating the need for further efforts of the design engineer to make the theoretical solution a feasible one. The cuckoo search, a nature-inspired metaheuristic algorithm, is used as the optimization algorithm in this study, and it is shown that the guaranteed global optimum solution is attained in a single run. Furthermore, a simple method is proposed to reduce the number of objective function and constraint calculations. The method is based on skipping calculations for design vectors recurring during the search process by use a caching technique. It is envisaged that the use of the proposed method will make a significant contribution to the streamlining of the quotation and design processes in the transformer industry as well as standardization of production materials.

Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixed integer nonlinear programming problem having a complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user requirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost while maximizing operating performance. This paper gives a detailed comparative analysis of the application of five modern nature-inspired metaheuristic optimization algorithms for the solution of the TDO problem, demonstrated on three test cases, and proposes two algorithms, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. It is expected that the use of these two algorithms would have a significant contribution to the reduction of the design and manufacturing costs of transformers.

Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey's natural gas consumption increased as well in parallel with the world's over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey's Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011-2014 monthly data were prepared and divided into two series. The first series is 2011-2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, orWinters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results. https://doi.org/10.3390/en9090727

Clinical decision support systems (C-DSS) provide supportive tools to the expert for the determination of the disease. Today, many of the support systems, which have been developed for a better and more accurate diagnosis, have reached a dynamic structure due to artificial intelligence techniques. However, in cases when important diagnosis studies should be performed in secret, a secure communication system is required. In this study, secure communication of a DSS is examined through a developed double layer chaotic communication system. The developed communication system consists of four main parts: random number generator, cascade chaotic calculation layer, PCM, and logical mixer layers. Thanks to this system, important patient data created by DSS will be conveyed to the center through a secure communication line.

Besides required courses which are compulsory for each student to be taken, universities also offer elective courses chosen by the students themselves. In their undergraduate study, since students are not guided about the elective courses, they lack information about the description and content of the course and generally fail to take the appropriate ones for their course of study. As a solution, using the knowledge of the previous required courses taken by the student it is possible to guide the student about elective courses appropriate for him/her. In this study, information from the transcripts of students are analyzed, and using this information a relationship is conducted between the required courses and the elective courses taken previously by the student. Rules are extracted by the help of data mining and an elective course suggestion system is developed using fuzzy logic. Successful results are obtained from the tests; it is observed that the students successful from the required courses are also successful in the related elective ones.

In Universities, controlling or organizing the senior projects, project proposals, thesis and internship of students is being very hard without an online system. If a project brings university and industry together it will be useful to society. But without a bridge this is not possible to students and industry owners. In this case an online system that simplifies processes and brings students, academics and industry together is needed. In this study a web based application is created. There are 5 types of users in this system and when industry has a project and needs employee then it add this project to the system and an academician would be a consultant to this project then students apply to this project. Owners of the industry can make comments about their student employers by this system and can send to the consultant. A case study of this management system was done in Computer Engineering department. (C) 2015 The Authors. Published by Elsevier Ltd.

In this study, a single-input single-output (SISO) neural generalized predictive control (NGPC) was applied to a three-joint robotic manipulator with a cubic trajectory and random disturbances. The SISO generalized predictive control (GPC) was also used for comparison. Modelling of the dynamics of the robotic manipulator was carried out by using the Lagrange-Euler equations. The frictional effects, random disturbance, carrying and falling load effects were added to the dynamics model. The cubic trajectory principle is used for position reference and velocity reference trajectories. A simulation program was prepared by using Delphi 5.0. All computations for the manipulator dynamics model, GPC_SISO, and NGPC_SISO were done on a PC with 733 MHz CPUs using this program. The parameter estimation algorithm used in the GPC_SISO is Recursive Least Squares. The minimization algorithm used in the NGPC-SISO is Newton-Raphson. According to the simulation outcome, the results from the NGPC_SISO algorithm were better than those from the GPC_SISO algorithm. And these results showed also that the NGPC-SISO reduced the influence of the load changes and disturbances. This means that the NGPC-SISO algorithm combines the advantages of predictive control and the neural network. (C) 2005 Elsevier B.V. All rights reserved.

In this study, the methods to apply the feed forward and Elman's recurrent neural networks for harmonic detection process in active filter are described. Generally, Fourier transformation is used to analyze a distorted wave from power line, and a low pass filter is used to eliminate the fundamental wave before each harmonic component is detected. Due to this complicated process, the behaviour of active filter is delayed such that it is difficult to compensate harmonic in real time. In order to improve the processing speed and simplify harmonic detection process, the feed forward and Elman's recurrent neural networks are used to detect harmonics from distorted wave instead of Fourier transformation and low-pass filter. We simulated the distorted wave including the 5th, 7th, 11th, 13th harmonics and used these neural networks to recognize each harmonic. The results show that these neural networks are applicable to detect each harmonic effectively. (C) 2004 Elsevier B.V. All rights reserved.

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.

Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetting phenomenon. In our previous work, integrating the SVM classifiers into an ensemble. framework using Learn++ (SVMLearn++) [11, we have shown that the SVM classifiers can in fact be equipped with the incremental learning capability. However, Learn++ suffers from an inherent out-voting problem: when asked to learn new classes, an unnecessarily large number of classifiers are generated to learn the new classes. In this paper, we propose a new ensemble based incremental learning approach using SVMs that is based on the incremental Leam++.MT algorithm. Experiments on the real-world and benchmark datasets show that the proposed approach can reduce the number of SVM classifiers generated, thus reduces the effect of outvoting problem. It also provides performance improvements over previous approach.

In this study, the application of the single input single output (SISO) neural generalized predictive control (NGPC) of a three joint robotic manipulator with the comparison of the SISO generalized predictive control (GPC) is presented. Dynamics modeling of the robotic manipulator was made by using the Lagrange-Euler equations. The frictional effects, the random disturbance, the state of carrying and falling load were added to dynamics model. The sinusoidal trajectory principle is used for position reference and velocity reference trajectories. The results show that the NGPC-SISO algorithm performs better than GPC-SISO algorithm and the influence of the load changes and disturbances to the NGPC-SISO is less than that of the GPC-SISO with sinusoidal trajectory.

In this study, the Elman's recurrent neural networks using conjugate gradient algorithm is used for harmonic detection. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) and resilient BP (RP) for training of the neural networks. The distorted wave including 5(th), 7(th), 11(th), 13(th) harmonics were simulated and used for training of the neural networks. The distorted wave including up to 25(th) harmonics were prepared for testing of the neural networks. The Elman's recurrent and feed forward neural networks were used to recognize each harmonic. The results of the Elman's recurrent neural networks are better than those of the feed forward neural networks. The conjugate gradient algorithm provides faster convergence than BP and RP algorithms in the harmonics detection.