Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed
نویسندگان
چکیده
منابع مشابه
Profilometry for the measurement of three-dimensional object shape using radial basis function, and multi-layer perceptron neural networks
Neural networks have been used to carryout calibration process in fringe projection profilometry for the measurement of three-dimensional object shape. The calibration procedure uses several calibration planes whose positions in space are known. Radial basis function based networks and multi-layer perceptron networks are investigated for the phase recovery. Preliminary studies are also presente...
متن کاملComparison of Multilayer Perceptron and Radial Basis Function networks as tools for flood forecasting
This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter,...
متن کاملThe Petri Net Radial Basis Function Perceptron
This paper introduces the Petri Net Radial Basis Function Perceptron (PNRBFP), a modified Petri Net that exhibits behavior equivalent to that of a typical radial basis function Perceptron when used in neural networking applications under certain domain restrictions. The PNRBFP makes use of modified transitions to perform basis function calculations and 'fuzzy' style tokens to transport values o...
متن کاملOn the use of back propagation and radial basis function neural networks in surface roughness prediction
Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...
متن کاملA comparison between Kriging and radial basis function networks for nonlinear prediction
Predictions by Kriging and radial basis function (RBF) networks with gaussian Kernels are compared. Kriging is a semi–parametric approach that does not rely on any specific model structure, which makes it much more flexible than approaches based on parametric behavioural models. On the other hand, accurate predictions are obtained for short training sequences, which is not the case for nonparam...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Water Resource and Protection
سال: 2012
ISSN: 1945-3094,1945-3108
DOI: 10.4236/jwarp.2012.410102