State estimation is essential to access observable NETWORK models for online monitoring and analyzing of power systems. Due to THE integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on THE possible correlation between THE real and pseudo measurements data. This study presents a new approach to model errors for THE distribution system state estimation purpose. In this paper, pseudo measurements are generated using a couple of real measurements data by means of THE artificial neural NETWORK method. In THE proposed method, THE radial basis function NETWORK with THE Gaussian kernel is also implemented to decompose pseudo measurements into several components. THE robustness of THE proposed error modeling method is assessed on IEEE 123-bus distribution test system where THE problem is optimized by THE imperialist competitive algorithm. THE results evidence that THE proposed method causes to increase in detachment accuracy of error components which results in presenting higher quality output in THE distribution state estimation.

An adaptive version of growing and pruning RBF neural NETWORK has been used to predict THE system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural NETWORK with growing and pruning capabilities is introduced to carry out on-line model identification. An Unscented Kalman Filter (UKF) algorithm with an exponential time-varying forgetting factor has been presented to enable THE neural NETWORK model to track any time-varying process dynamic changes. An adaptive NMPC has been designed based on THE sequential quadratic programming technique. THE paper makes use of a dynamic linearization approach to extract a linear model at each sampling time instant so as to develop an adaptive LMPC. THE servo and regulating performances of THE proposed adaptive control schemes have been illustrated on a non-linear Continuous Stirred Tank Reactor (CSTR) as a benchmark problem. THE simulation results demonstrate THE capability of THE proposed identification strategy to effectively identify compact, accurate and transparent model for THE CSTR process. It is shown that THE proposed adaptive NMPC controller presents better improvement with faster response time for both servo and regulatory control objectives in comparison with THE proposed adaptive LMPC, an adaptive generalized predictive controller based on Recursive Least Squares (RLS) algorithm and well-tuned PID controllers.

One of important aspects of software project management is required cost and time estimation for developing THE information system.One of THE important concerns for a project manager is estimation of required effort for development of an information system.THErefore many effort estimation models have been proposed. Learning- based methods, such as neural NETWORKs, is one of THEm.THE aim of this study is use of RBF neural NETWORKs to estimate THE necessary effort which is required for developing an information system.THE result of this study shows that, this NETWORK in comparing with THE model-based methods gives a suitable estimation about required effort for development of THE system.

Data acquisition of chemical engineering processes is expensive and THE collected data are always contaminated with inevitable measurement errors. Efficient algorithms are required to filter out THE noise and capture THE true underlying trend hidden in THE training data sets.egularization NETWORKs, which are THE exact solution of multivariate linear regularization problem, provide appropriate facility to perform such a demanding task. THEse NETWORKs can be represented as a single hidden layer neural NETWORK with one neuron for each distinct exemplar. Efficient training of Regularization NETWORK requires calculation of linear synaptic weights, selection of isotropic spread (s) and computation of optimum level of regularization (l*). THE latter parameters (s and l*) are highly correlated with each oTHEr. A novel method is presented in this article for development of a convenient procedure for de-correlating THE above parameters and selecting THE optimal values of l* and. s* THE plot of l* versus s suggests a threshold s* that can be regarded as THE optimal isotropic spread for which THE Regularization NETWORK provides appropriate model for THE training data set. It is also shown that THE effective degrees of freedom of a Regularization NETWORK are a function of both regularization level and isotropic spread. A readily calculable measure of THE approximate degrees of freedom of a Regularization NETWORK is also introduced which may be used to de-couple l* and s.

A new learning strategy is proposed for training of radial basis functions (RBF) NETWORK. We apply two different local optimization methods to update THE output weights in training process, THE gradient method and a combination of THE gradient and Newton methods. Numerical results obtained in solving nonlinear integral equations show THE excellent performance of THE combined gradient method in comparison with gradient method as local back propagation algorithms.

Transmission of compressed video over error prone channels may result in packet losses, which can degrade THE image quality. Error concealment (EC) is an effective approach to reduce THE degradation caused by THE missed information. THE conventional temporal EC techniques are always inefficient when THE motions of THE video object are irregular. In this paper, in order to overcome this problem, an efficient temporal EC approach to conceal THE macroblock error for video coding systems is proposed. THE proposed EC method employs a RBF neural NETWORK to estimate THE motion vectors of THE damaged macroblocks. RBF estimator is used only for areas of THE fast motions, which reduces computation complexity. Because THE neural NETWORKs have a great capacity for visualizing and interpreting high-dimensional data sets, THE estimation model proposed herein can exploit THE nonlinearity property of THE neural NETWORKs to estimate lost motion vectors more accurately. Simulation results show that THE proposed technique enhances both subjective and objective quality of reconstructed frames, such as THE average PSNR increases about 1.5 dB compared to THE BMA method for THE test video sequences in some frames.

Estimating THE spatial distribution of weeds for site-specific control is essential. THErefore, this research was conducted to predict and interpolate THE spatial distribution of Amaranthus retroflexus L. populations using a Radial Basis Function Neural NETWORK (RBF-NN) in two potato fields. Weed population data were collected from sampling 200 and 36 points, respectively, in two commercial potato fields in Jolge Rokh, of Torbat Heidarieh in Khorasan Razavi and Mojen of Shahroud in Semnan Provinces, Iran, in 2012. Some statistical tests, such as comparisons of THE means, variance and statistical distribution, as well as linear regression, were used for THE observed point sample data and THE estimated weed seedling density surfaces to evaluate THE neural NETWORK capability for predicting THE spatial distribution of THE weed. THE results showed that THE trained RBF-NN had high capability in THE spatial prediction in points that were not sampled with 100% output, 0. 999 coefficients, and an average error of less than 0. 04 and 0. 07 in THE Mojen and Jolge Rokh Regions, respectively. Test results also showed that THEre was no significant difference between THE statistical characteristics of actual data and THE values predicted by THE RBF-NN. According to THE experimental results, THE RBFNN can be used as an alternative method to estimate THE spatial changes function of annual weeds with random dispersion, such as Redroot Pigweed.

Introduction: Presently, THE loss of ground water levels and THE increase in dissolved salts have given importance to THE determination of salinity and THE management of THEir variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. THE direct electrode contact method (Wenner method) is one of THE widely used methods to rapidly measure soil ECa in farms. However, soil scientists prefer soil actual electrical conductivity (saturated extract electrical conductivity) (ECe) as an indicator of soil salinity, though its measurement is only possible in THE laboratory. THE aim of this study was to find a relationship between THE prediction of soil actual electrical conductivity (ECe) in terms of temperature, moisture, bulk density and apparent electrical conductivity of soil (ECa). THEreby, THE estimation of ECe would allow THE partial calculation of ECa that is dependent upon soil salinity and dissolved salts. Materials and Methods: This study used RBF neural NETWORK in Box-Behnken statistical design to explore THE impacts of effective parameters on direct contact method in THE measurement of soil ECa and provided a model to estimate ECe from ECa, temperature, moisture content and bulk density. In this study soil apparent electrical conductivity (ECa) was measured by direct contact (Wenner) method. THE present study considered four most effective factors: ECa (saturated paste extract EC), moisture, bulk density, and temperature (Baradaran Motie et al., 2010). Given THE characteristics of farming soils in Khorasan Razavi Province (Iran), THE maximum and minimum of each independent variable were assumed as 0. 5-6 mS. cm-1 for ECe, 5-25% for moisture content, 1-1. 8 g. cm-3 for bulk density, and 2-37° C for soil temperature. Considering THE experimental design, three moisture levels (5, 15 and 25%), three salinity levels (0. 5, 3. 25 and 6 mS. cm-1), three temperature levels (2, 19 and 37° C) and three compaction levels with bulk densities of 1, 1. 4 and 1. 8 g. cm-3 were assumed in 27 trials with predetermined arrangement on THE basis of Box-Behnken technique. 13 common algorithms were explored in MATLAB software package for THE training of THE artificial neural NETWORK in order to find THE optimum algorithm (Table 4). THE input layer of THE NETWORK designed by integrating a Randomized Complete Block Design (RCBD) with k-fold cross-validation. Using k-fold cross-validation, 20 different datasets were generated for training and validation of RBF neural NETWORK. Results and Discussion: A combination of an RCBD and k-fold cross-validation was used. THE results of both training and validation phases should be considered in THE selection of training algorithm. In addition, R2 of T1 training algorithm had a much lower standard deviation than oTHEr training algorithms. THE lower standard deviation is, THE more capable THE algorithm would be in learning from different datasets. Considering all aspects, trainbr (T2) training algorithm was found to have THE best performance among all 13 training algorithms of THE neural NETWORK. Table 7 tabulates THE results of means comparison for R2 of RBF model for both training and validation phases resulted from THE application of some combinations of S and L2 factors as interaction. As can be observed, R2 = 0. 99 for all of THEm with no significant difference. However, THE magnitude of order differed between training and validation phases. Given THE importance of THE training phase, L2=9 and S=0. 1 were regarded as THE optimum values. THE sensitivity analysis of THE NETWORK revealed that soil ECa, moisture, bulk density, and temperature had THE highest to lowest impact on THE estimation of soil ECe, respectively. This model can improve THE precision of soil ECa measurement systems in THE estimation and preparation of soil salinity maps. FurTHErmore, this model can save in time of data analysing and soil EC mapping because it does not need data recollection for THE calibration of systems. A validation prose was done with a 60 field collected data set. THE results of validation show R2=0. 986 between predicted and measured ECa. Conclusions: THE present research focused on improving THE precision of soil ECe measurement on THE basis of easily accessible parameters (ECa, temperature, moisture, and bulk density). In conventional methods of soil EC mapping, THE systems only measure soil ECa and THEn calibrate it to ECe by collecting some samples and using statistical methods. In this study, Soil ECe was estimated with R2 = 0. 99 by a multivariate artificial neural NETWORK model with THE inputs, including ECa, temperature, moisture, and bulk density of soil without any need to collect furTHEr soil samples and calibration process. THE Bayesian training algorithm was introduced as THE best training algorithm for this neural NETWORK. THEreby, soil EC variation maps can be prepared with higher precision to estimate THE spatial spread of salinity in farms. Also, THE results imply that soil ECa, moisture, bulk density and temperature have THE highest to lowest effectiveness on THE estimation of soil ECe, respectively.

Since 1993, Devices based on CNTs have applicationsranging from nanoelectronics to optoelectronics. THEchallenging issue in designing THEse devices is that THEnonequilibrium Green's function (NEGF) method has tobe employed to solve THE Schrödinger and Poissonequations, which is complex and time consuming. In THEpresent study, a novel smart and optimal algorithm ispresented for fast and accurate modeling of CNT fieldeffecttransistors (CNTFETs) based on an artificial neuralNETWORK. A new and efficient way is presented forincrementally constructing radial basis function (RBF)NETWORKs with optimized neuron radii to obtain THEestimator NETWORK. An incremental extreme learningmachine (I-ELM) algorithm is used to train THE RBFNETWORK. To ensure THE optimal radii for incrementalneurons, this algorithm utilizes a modified version of anoptimization algorithm known as THE Nelder-Meadsimplex algorithm. Results confirm that THE proposedapproach reduces THE NETWORK size for faster errorconvergence while preserving THE estimation accuracy.