application of generalized regression neural network in

Application of generalized regression neural network

In this paper a method of generalized regression neural network residual kriging (GRNNRK) was presented for terrain surface interpolation The GRNNRK was a two-step algorithm The first step included estimating the overall nonlinear spatial structures by generalized regression neural network (GRNN) and the second step was the analysis of the

Application of radial basis function and generalized

2006-10-14 1 3 Generalized regression neural network (GRNN) A general regression neural network (GRNN) Specht's term for Nadaraya–Watson kernel regression also reinvented in the NN literature by Schioler and Hartmann does not require an iterative training procedure It approximates any arbitrary function between input and output vectors

Application of Generalized Regression Neural

Such generalized regression neural network architecture was trained tested and validated with real-time experimental variable data sets The results of the GRNN model are in good agreement with experimental results The overall accuracy of the proposed GRNN model in predicting the performance is 96 29%

The application of generalized regression neural

2002-8-21The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets As model formulations 10 kinds of aspirin matrix tablets were prepared Eudragit RS PO was used as matrix substance The amount of Eudragit RS PO and compression pressure were selected as causal factors

Application of the radial basis function neural network to

2020-1-17In this paper we use the radial basis function neural network or RBFN to predict reservoir log properties from seismic attributes We also compare the results of this approach with the use of the generalized regression neural network GRNN for the same problem as proposed by Hampson et al (2001) We discuss both the theory behind these

Use of Generalized Regression Neural Network in

2013-1-193 GENERALIZED REGRESSION NEURAL NETWORK The Generalized Regression Neural Network is a kernel based feed forward neural network with an architecture having three layers namely the input hidden and output layers (Powell 1992 Wasserman 1989 1993) The Generalized Regression Neural Networks use the kernel or Basis Function method

The application of generalized regression neural

The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets As model formulations 10 kinds of aspirin matrix tablets were prepared Eudragit RS PO was used as matrix substance The amount of Eudragit RS PO and compression pressure were selected as causal factors

Use of Generalized Regression Neural Network in

2013-1-193 GENERALIZED REGRESSION NEURAL NETWORK The Generalized Regression Neural Network is a kernel based feed forward neural network with an architecture having three layers namely the input hidden and output layers (Powell 1992 Wasserman 1989 1993) The Generalized Regression Neural Networks use the kernel or Basis Function method

Generalized regression neural networks for

2018-10-4reported in the literature that addresses the application of generalized regression neural networks (GRNN) to ET0 estimation This provided an impetus to investigate the potential of the GRNN for better mapping of the process The potential of the GRNN in modelling of ET0 is investigated and discussed in the present study

Neural Networks and Statistical Models

2012-8-9neural networks and statistical models such as generalized linear models maximum redundancy analysis projection pursuit and cluster analysis Introduction Neural networks are a wide class of flexible nonlinear regression and discriminant models data reduction models and nonlinear dynamical systems They consist of an often large number of

A Generalized Regression Neural Network Model for Path

2016-6-4A Generalized Regression Neural Network Model for Path Loss Prediction at 900 MHz for Jos City Nigeria Deme C Abraham Department of Electrical and Computer Engineering Ahmadu Bello University Zaria Nigeria ABSTRACT: This study considers the application of a Generalized Regression Neural Network (GR-NN)

Generalized Regression Neural Network: an

Generalized Regression Neural Network: an Alternative Approach for Reliable Prognostic Analysis of Spatial Signal Power Loss in Cellular Broadband Networks [21] V C Ebhota Isabona J and Srivastava V M (2018) Improved Adaptive Signal Power loss Prediction using Combined Vector Statistics based Smoothing and Neural Network approach

PAPER OPEN ACCESS The safety evaluation of

generalized regression neural network and the radial basis function neural network can better evaluate and predict the safety of chemical enterprises The prediction of radial basis function neural network is more accurate 1 Introduction Chemical industry plays an important role in the economy For example the GDP of chemical

Generalized Regression Neural Network and Radial

In this paper two types of Artificial Neural Network (ANNs) Generalized Regression Neural Network (GRNN) and Radial Basis Function (RBF) have been used for heart disease to prescribe the medicine Diagnosing the heart disease and prescribing the medicine on the basis of symptoms is a very challenging task to improve the ability of the physicians

Generalized Regression Neural Network

Generalized regression neural network (GRNN) is a kind of ANN and is applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation In the case a method named WPT-based generalized regression neural network (WPTGRNN) was used for analyzing overlapping spectra

QSAR/QSPR studies using probabilistic neural

The Probabilistic Neural Network (PNN) and its close relative the Generalized Regression Neural Network (GRNN) are presented as simple yet powerful neural network techniques for use in Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) studies

Loan Default Prediction Report

2015-9-293 1 Generalized Regression Neural Network The first model we used is the Generalized Regression Neural Network (GRNN) which is a kind of neural network that specializes in solving function approximation problems (Ahangar Yahyazadehfar Pournaghshband 2010) The GRNN model is

Generalized regression neural network

2018-6-15In this study a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath

Applications of General Regression Neural Networks

2018-6-13The general regression neural network (GRNN) is a single-pass neural network which uses a Gaussian activation function in the hidden layer GRNN consists of input hidden summation and division layers The regression of the random variable y on the observed values X of random variable x can be found using

Exploratory Application of Neural Networks to School

2011-6-29Exploratory Application of Neural Networks to School Finance Forecasting Educational Spendi_。 This study provides a side by side comparison of linear regression methodologies used by the National Center for Education Statistics in preparing projections of

Application of Generalized Regression Neural

The majority of artificial neural network (ANN) applications to water resources data employ the feed-forward back-propagation (FFBP) method This study used an ANN algorithm the generalized regression neural network (GRNN) for intermittent river flow forecasting and estimation

Review of Applications of Generalized Regression Neural

2018-5-30[2] D F Specht "A general regression neural network " IEEE transactions on neural networks vol 2 no 6 pp 568–576 1991 [3] A J Al-Mahasneh S G Anavatti and M A Garratt "Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks