Objective: Soil temperature serves as a key variable in hydrological investigations to determine soil moisture content as well as hydrological balance in watersheds. The ingoing research aims to shed lights on potential of artificial neural networks (ANNs) and Neuro-Fuzzy inference system (ANFIS) to simulate soil temperature at 5-100 cm depths. To satisfy this end, climatic and...

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, ...

pomegranate is an important iranian-native fruit, with many varieties cultivated. although the volume of data on the importance of pomegranates in human nutrition has increased tremendously in the last years, the physical properties of the pomegranate fruit during fruit maturity have not yet been studied in detail. thus, the present study aimed to evaluate changes in physical characteristics of...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image ...

Xinjie Wu School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China,[email protected] Abstract Extreme learning machine (ELM) is an efficient algorithm for single-hidden layer feedforward neural networks (SLFNs), which can produce good generalization performance in most cases and learn thousands of times faster than conventional p...

We used an advanced computer logic system (NETS 3.0) to decipher electromagnetic (EM) scans in lieu of traditional linear regression for estimation of pork carcass composition. Fifty EM scans of pork carcasses were obtained on-line (prerigor) at a swine slaughter facility. Right sides were cut into wholesale parts and dissected into fat, lean, and bone to obtain total dissected carcass and prim...

Extreme learning machine (ELM) is a type of randomized neural networks originally developed for linear classification and regression problems in the mid-2000s, has recently been extended to computational partial differential equations (PDE). This method can yield highly accurate solutions linear/nonlinear PDEs, but requires last hidden layer network be wide achieve high accuracy. If narrow, acc...