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Robust radial basis function neural networks

Chien-Cheng Lee, Pau-Choo Chung, Jea-Rong Tsai, Chein-I Chang
1999 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
In the training stage an Annealing Robust Learning Algorithm (ARLA) has been used for make the networks robust against noise and outliers.  ...  Radial Basis Function Neural Network (RBFNN) is considered as a good applicant for the prediction problems due to it's fast convergence speed and rapid capacity of learning, therefore, has been applied  ...  Conclusion In this paper, the radial basis function networks with the support vector regression and the robust learning algorithm is developed for the system identification of nonlinear plant with outliers  ... 
doi:10.1109/3477.809023 pmid:18252348 fatcat:fnpekr7ekzfnjbd7sc6lc62clq

Robust Learning Algorithm Based on Iterative Least Median of Squares

Andrzej Rusiecki
2012 Neural Processing Letters  
Improved robustness of our novel algorithm, for data sets with varying degrees of outliers, is shown.  ...  We demonstrate how to minimise new non-differentiable performance function by a deterministic approximate method. Results of simulations and comparison with other learning methods are demonstrated.  ...  Some efforts to make the learning methods of radial basis function networks more robust, following the approaches for the sigmoid networks, have been also made [3, 4] .  ... 
doi:10.1007/s11063-012-9227-z fatcat:ynxqinc7rbbgxcyayjvr5fhnc4

Machine Learning: A Crucial Tool for Sensor Design

Weixiang Zhao, Abhinav Bhushan, Anthony Santamaria, Melinda Simon, Cristina Davis
2008 Algorithms  
For each method, the principles and the key issues that affect modeling results are discussed.  ...  As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design.  ...  A method integrating a linear outlier detection strategy with radial basis function neural networks (RBFNN) was proposed to detect outliers in complex nonlinear systems [20] .  ... 
doi:10.3390/a1020130 pmid:20191110 pmcid:PMC2828765 fatcat:qnxufvx5djaajlqr6wsi5ltg3e


Saadi Ahmad Kamaruddin, Nor Azura Md Ghani, Norazan Mohamed Ramli
2016 Jurnal Teknologi  
However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value.  ...  ) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data.  ...  Some efforts to make the learning methods of radial basis function networks more powerful, following the approaches for the sigmoid networks, have also been exercised [15, 16] .  ... 
doi:10.11113/jt.v78.10024 fatcat:sukywrcmejd7dohkdmuz2gocse

Iterative Blind Image Motion Deblurring via Learning a No-Reference Image Quality Measure

Wen-Hao Lee, Shang-Hong Lai, Chia-Lun Chen
2007 2007 IEEE International Conference on Image Processing  
The motion blur parameters are first approximately estimated from the robust global motion estimation result.  ...  Note that a no-reference image quality assessment model is learned by training a RBF neural network from a collection of representative training images simulated with different motion blurs.  ...  The second layer is the hidden radial basis layer. Each neuron in this layer utilizes a radial basis function (the Gaussian function for example) as the activation function.  ... 
doi:10.1109/icip.2007.4380040 dblp:conf/icip/LeeLC07 fatcat:thd4rzodvzbnrk3wuverue4gn4

Neural intelligent control for a steel plant

G. Bloch, F. Sirou, V. Eustache, P. Fatrez
1997 IEEE Transactions on Neural Networks  
In Section III, the optimal thermal cycle of alloying is determined using a radial basis function neural network, from a static database built up from recorded measurements.  ...  Robust learning criteria are used to tackle possible outliers in the database. The neural network is then pruned in order to enhance the generalization capabilities.  ...  This estimation is achieved by using a radial basis function (RBF) neural network, which predicts, from the operating conditions and the features of the steel sheet, the thermal energy required for correct  ... 
doi:10.1109/72.595889 pmid:18255694 fatcat:pam2rzseb5amtiqvdorm6qhsoi

An efficient MDL-based construction of RBF networks

Aleš Leonardis, Horst Bischof
1998 Neural Networks  
We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection.  ...  We test the proposed method on function approximation and classification tasks, and compare it with some other recently proposed methods. ᭧  ...  We are grateful for the reviewer's constructive comments which helped considerably in improving the quality of the paper.  ... 
doi:10.1016/s0893-6080(98)00051-3 pmid:12662797 fatcat:m7p5rvqamzfkzagy2tdmzgjg2u

Recent advances in industrial control

Hao Yu, Tiantian Xie, Bogdan Wilamowski
2011 IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society  
, neural networks, radial basis function networks, sliding-mode based control and so on.  ...  For each paper, a brief summary is given to introduce the related control technologies and applications.  ...  A radial basis function neural network with a robust error estimator was adopted to approximate the nonlinear dynamics of the robotic manipulator.  ... 
doi:10.1109/iecon.2011.6120073 fatcat:x4k7jgsxxvek3obmrmpngldceu

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

Alireza Rostami, Mahdi Kalantari-Meybodi, Masoud Karimi, Afshin Tatar, Amir H. Mohammadi
2018 Oil & Gas Science and Technology  
In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network  ...  Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations.  ...  Conclusion In this study, Multilayer Perceptron (MLP) neural network, Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural  ... 
doi:10.2516/ogst/2018006 fatcat:hwogbhlh6zgq3aqqp627btbieq

Robust Full Bayesian Learning for Radial Basis Networks

Christophe Andrieu, Nando de Freitas, Arnaud Doucet
2001 Neural Computation  
We propose a hierarchical full Bayesian model for radial basis networks.  ...  This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis function.  ...  Vermaak (Cambridge University), Chris Holmes (Imperial College of London), David Lowe (Aston University), David Melvin (Cambridge Clinical School), Stephen Roberts, and Will Penny (University of Oxford) for  ... 
doi:10.1162/089976601750541831 pmid:11571002 fatcat:yinyoccowzguzp23nr5t2nraaa

Diagnosing Breast Cancer Based on the Adaptive Neuro-Fuzzy Inference System

S. Chidambaram, S. Sankar Ganesh, Alagar Karthick, Prabhu Jayagopal, Bhuvaneswari Balachander, S. Manoharan, David Becerra-Alonso
2022 Computational and Mathematical Methods in Medicine  
The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS).  ...  Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance.  ...  Acknowledgments Alagar Karthick gratefully acknowledges the group FQM-383 from Universidad de Cordoba, Spain, for the provision of an honorary visiting research position in the group.  ... 
doi:10.1155/2022/9166873 pmid:35602339 pmcid:PMC9117043 fatcat:dp7a2nb4ofe2ff7jxvsyzz42ny

Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing

Hyeon-Kyu Park, Jae-Hyeok Lee, Jehyun Lee, Sang-Koog Kim
2021 Scientific Reports  
Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets  ...  The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule.  ...  In this work, a radial basis function φ(x i ) = exp(−γ �x i � 2 ) was employed as the kernel function.  ... 
doi:10.1038/s41598-021-83315-9 pmid:33589666 fatcat:ct5b7nadwzhqjlmuahlegd4vmy

A Stochastic Approach to Diffeomorphic Point Set Registration with Landmark Constraints

Ivan Kolesov, Jehoon Lee, Gregory Sharp, Patricio Vela, Allen Tannenbaum
2016 IEEE Transactions on Pattern Analysis and Machine Intelligence  
This work presents a deformable point set registration algorithm that seeks an optimal set of radial basis functions to describe the registration.  ...  A novel, global optimization approach is introduced composed of simulated annealing with a particle filter based generator function to perform the registration.  ...  Acknowledgments This project was supported by in part by grants from the National Center for Research Resources (P41-RR-013218) and the National Institute of Biomedical Imaging and Bioengineering (P41-  ... 
doi:10.1109/tpami.2015.2448102 pmid:26761731 pmcid:PMC4727970 fatcat:xc3vnwzwqzbcbmlazzj24z4xde

High-Precision 3D Reconstruction for Small-to-Medium-Sized Objects Utilizing Line-Structured Light Scanning: A Review

Bin Cui, Wei Tao, Hui Zhao
2021 Remote Sensing  
Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details  ...  However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements  ...  This algorithm can avoid the trivial solution that appears when the radial basis function is approximated, which has good robustness and effectiveness for processing large-scale shape reconstruction without  ... 
doi:10.3390/rs13214457 fatcat:6pnpg7rxcnaufcrbey2m335lnq

Machine Learning Techniques for Short-Term Electric Power Demand Prediction

Fernando Mateo, Juan José Carrasco, Mónica Millán-Giraldo, Abderrahim Sellami, Pablo Escandell-Montero, José María Martínez-Martínez, Emilio Soria-Olivas
2013 The European Symposium on Artificial Neural Networks  
In this paper, several Machine Learning techniques are evaluated and compared with a linear technique (Robust Multiple Linear Regression) and a naïve method.  ...  Although there exist several works that deal with this issue, it remains open.  ...  The best choice for the MLP size is listed in Table 2 . For the LS-SVM models, radial basis function (RBF) kernels were used.  ... 
dblp:conf/esann/MateoCMSEMS13 fatcat:77lxmje6zzfxhmezq6vmk42kzy
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