A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape
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 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 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 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 کاملNovel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...
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: Ibn AL- Haitham Journal For Pure and Applied Science
سال: 2018
ISSN: 2521-3407,1609-4042
DOI: 10.30526/31.2.1950