Product Identification Using Image Processing and Radial Basis Function Neural Networks

2015 ◽  
Vol 761 ◽  
pp. 120-124
Author(s):  
K.A.A. Aziz ◽  
Abdul Kadir ◽  
Rostam Affendi Hamzah ◽  
Amat Amir Basari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Architecture ◽  
Rbf Neural Networks ◽  
Specific Product ◽  
Product Identification ◽  
Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 56-80 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Neural Networks ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Basis Functions ◽  
Second Phase ◽  
Rbf Neural Networks ◽  
Radial Basis ◽  
Two Phases

In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.

Descartes' Rule of Signs for Radial Basis Function Neural Networks

2002 ◽  
Vol 14 (12) ◽  
pp. 2997-3011 ◽  
Author(s):  
Michael Schmitt
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Lower Bounds ◽  
Basis Function ◽  
Rbf Neural Networks ◽  
Vc Dimension ◽  
Network Parameters ◽  
Number Of Zeros ◽  
Radial Basis

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previous work showing that RBF neural networks with two or more input nodes have superlinear VC dimension. The rules also give rise to lower bounds for network sizes, thus demonstrating the relevance of network parameters for the complexity of computing with RBF neural networks.

Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks

2016 ◽  
Vol 60 ◽  
pp. 151-164 ◽  
Author(s):  
Afshin Tatar ◽  
Saeid Naseri ◽  
Mohammad Bahadori ◽  
Ali Zeinolabedini Hezave ◽  
Tomoaki Kashiwao ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Ionic Liquids ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Networks ◽  
Radial Basis

scholarly journals Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF) neural networks

2016 ◽  
Vol 3 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Tatar Afshin ◽  
Barati-Harooni Ali ◽  
Moslehi Hossein ◽  
Naseri Saeid ◽  
Bahadori Meysam ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Gas ◽  
Radial Basis Function ◽  
Basis Function ◽  
Formation Temperature ◽  
Rbf Neural Networks ◽  
Water Formation ◽  
Radial Basis

scholarly journals A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape

2018 ◽  
Vol 31 (2) ◽  
pp. 210
Author(s):  
Laith Jasim Saud ◽  
Zainab Kudair Abass
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Rbf Neural Network ◽  
Human Detection ◽  
Geometric Information ◽  
Radial Basis ◽  
Mlp Model

       Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.

scholarly journals Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 378
Author(s):  
Kayol Soares Mayer ◽  
Jonathan Aguiar Soares ◽  
Dalton Soares Arantes
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Multiple Input Multiple Output ◽  
Low Earth Orbit ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Rbf Neural Networks ◽  
Radial Basis

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.

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