Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical Algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil Mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic Algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The Algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR Algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR Algorithm in high precision s

We present a novel Algorithm, which is called Cutting Algorithm (CA), for improving the accuracy and reducing the computations of the Least Squares Support Vector Machines (LS-SVMs). The method is based on dividing the original problem to some subproblems. Since a master problem is converted to some small problems, so this Algorithm has fewer computations. Although, in some cases that the typical LS-SVM cannot classify the dataset linearly, applying the CA the datasets can be classified. In fact, the CA improves the accuracy and reduces the computations. The reported and comparative results on some known datasets and synthetics data demonstrate the efficiency and the performance of CA.

Rivers are the most important water supply resource for the drinkable, agricultural and industrial demands. Therefore, Estimation of water quality parameters in rivers is an essential and necessary task. This research applies the Adaptive Neuro-Fuzzy Inference System (ANFIS), the Least Squares-Support Vector Machines (LS-SVM) and the Gene Expression Programming (GEP) for Estimation of Total Dissolved Solids (TDS), Electrical Conductivity (EC) and Total Hardness (TH) in the Sepidrood River and a 40 year period. The applied performance criteria are the correlation coefficient (R), the Nash-Sutcliffe model Efficiency coefficient (NSE), the Normalized Mean Squared Error (NMSE) and the Mean Absolute Error (MAE). These methods have high ability for Estimation of water quality parameters. The best method is LS-SVM method for Estimation of TDS (RTrain=0. 95 RTest=0. 96). The best method is GEP method for Estimation of EC (RTrain=0. 94 RTest=0. 95). The best method is ANFIS method for Estimation of TH (RTrain=0. 92 RTest=0. 94). This research shows that intelligence methods can estimate unmeasured concentration of qualitative parameters by concentration of other qualitative parameters.

Because of the high cost of mechanical and optical gyroscopes, in recent years the low cost measurement systems based on Micro Electro Mechanical sensor (MEMS) are widely used. These systems often are integrated with radio navigation to achieve the required accuracy. MEMS gyroscopes suffer some problems like large drift and acoustic sensitivity, compare with MEMS accelerometers. So the design of low cost inertial measurement system without using the gyroscope, and based on a suitable geometry configuration of accelerometers is considered in literature in recent years. The goal of this research is to design and manufacture of such free gyroscope measurement unit. For this purpose, first an appropriate geometry configuration of accelerometer is proposed then the mathematical relationship between the output of linear accelerometers and linear and revolving acceleration of the center of mass of the structure is derived and then the angular velocities are estimated. The proposed free gyroepocs IMU is constructed using the ADXL345 accelerometers as sensors and the ARM-LPC1768 microcontroller as processing unit. The experimental results using a rotating table as reference are gathered in the Lab. The results show the suitable performance of the measurements unit.

This paper presents an improved global-best harmony search (IGHS) method to estimate the harmonics in power systems. It utilizes IGHS to estimate the harmonic components of a distorted signal along with adaptive noise. The harmonic Estimation problem is linear in amplitudes and nonlinear in phases and frequencies. IGHS is used to estimate phases and frequencies, whereas the least square (LS) method is used to estimate the amplitudes. The improvements in error, as well as the running time compared with the conventional discrete Fourier transform, genetic Algorithm method, particle swarm optimizer with passive congregation (PSOPC) and Fuzzy Bacterial Foraging (FBF) is explained in this paper. The results show that new method is able to estimate integral harmonics, inter-harmonics and sub-harmonics even in the presence of fundamental frequency deviation and decaying DC component with quite acceptable performance.

Modified Normalized Least Mean Square (MNLMS) Algorithm, which is a sign form of NLMS based on set-membership (SM) theory in the class of optimal bounding ellipsoid (OBE) Algorithms, requires a priori knowledge of error bounds that is unknown in most applications. In a special but popular case of measurement noise, a simple Algorithm has been proposed. With some simulation examples the performance of Algorithm is compared with MNLMS.