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Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

Xing Zong-yi, Qin Yong, Pang Xue-miao, Jia Li-min, Zhang Yuan
2010 Mathematical Problems in Engineering  
In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network.  ...  The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of  ...  Acknowledgments The authors wish to thank the anonymous reviewers for their extensive and very helpful comments and guidance provided in making the paper more acceptable.  ... 
doi:10.1155/2010/124014 fatcat:erbi2rlrvjbgbkho2zgiknrsem

A Survey on Applications of Neural Networks and Genetic Algorithms in Fault Diagnostics for Antenna Arrays

Subhash Mishra
2013 IOSR Journal of Electrical and Electronics Engineering  
In this review, the applications of neural networks and genetic algorithms in fault diagnosis of antenna arrays are summarized.  ...  Genetic algorithms have also been applied very successfully in locating faults in the antenna array.  ...  Acknowledgements We express our sincere thanks to all the authors and the publishers of the books, research/reviews papers and articles referred in this manuscript.  ... 
doi:10.9790/1676-0862732 fatcat:4e7ukujyxrb3pg53j7rsiybs3a

Radial Basis Function Neural Network Based on PSO with Mutation Operation to Solve Function Approximation Problem [chapter]

Xiaoyong Liu
2010 Lecture Notes in Computer Science  
This paper presents a novel learning algorithm for training and constructing a Radial Basis Function Neural Network (RBFNN), called MuPSO-RBFNN algorithm.  ...  PSO with mutation operation and genetic algorithm are respectively used to train weights and spreads of oRBFNN, which is traditional RBFNN with gradient learning in this article.  ...  The author is thankful to the reviewers who provided valuable comments that greatly improved the quality of this article.  ... 
doi:10.1007/978-3-642-13498-2_13 fatcat:eqzrqwsornfjpjaifjzdxme5uq

Rule Extraction from Radial Basis Functional Neural Networks by Using Particle Swarm Optimization

M. R. Senapati, I. Vijaya, P.K. Dash (SMIE
2007 Journal of Computer Science  
Our Simulation results using Radial Basis Functional Neural Networks (RBFNN) was applied to the PAT, WBC and IRIS data sets as a classification problem to illustrate the new knowledge extraction technique  ...  Radial basis functional neural networks (RBFNN) provide an outstanding possibility for generating rules for solving pattern classification problems.  ...  INTRODUCTION A Radial Basis Functional neural network (RBFNN) is trained to perform a mapping from an mdimensional input space to an n-dimensional output space.  ... 
doi:10.3844/jcssp.2007.592.599 fatcat:hcgemaqbgfcrdoyxk4lhfl6zju

Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

Iftikhar Ahmad, Rayan Atteah Alsemmeari
2020 Computers Materials & Continua  
Here, Well-known activation functions like: sine, sigmoid and radial basis are explored, investigated and applied to measure their performance on the GA (Genetic Algorithm) features subset and with full  ...  An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information.  ...  Acknowledgment: This project was funded by the Deanship of Scientific Research (DSR)  ... 
doi:10.32604/cmc.2020.011732 fatcat:t6y6syfuq5bzjllomtr5ihtvea

Neural Network Radial Basis Function classifier for earthquake data using aFOA

Anurag Rana, Arjun Kumar, Ankur Sharma
2016 International Journal of Advanced Research  
radial basis function:-NN RBF a nearest neighbor classifier. It uses Gaussian transfer functions having radial symmetry [13].  ...  The centers and widths of the Gaussian (radial basis functions) are set by unsupervised learning rules, and supervised learning is applied to (cluster centers), because it is problem dependent.  ... 
doi:10.21474/ijar01/1244 fatcat:gjctq4mktnbq3hnvyyugctjhqq


A. Golbabai, M. Moini
2013 International Journal of Pure and Applied Mathematics  
This paper proposes an approach for the construction of width factor using genetic algorithm to optimize the Gaussian function in RBF networks.  ...  Conventionally, artificial neural network technique is used to predict the chaotic time series.  ...  For this, we apply genetic algorithm to obtain q corresponds to smallest root mean square error (3).  ... 
doi:10.12732/ijpam.v83i3.2 fatcat:wzkp5ucyd5h5tclgcxu7ut3vkm

Neural Network for Kidney Stone Detection

2016 International Journal of Science and Research (IJSR)  
In this paper, two neural network algorithms viz Radial basis function and Learning vector quantization are used for diagnosis purpose Also a comparison is made between the two algorithms using MATLAB  ...  Kidney stone detector proves to be a major challenge for detecting the kidney stone disease.  ...  In this paper two neural network algorithms i.e radial basis function and learning vector quantization are used for detecting a kidney stone. Firstly two algorithms are used for training the data.  ... 
doi:10.21275/v5i4.nov162742 fatcat:qc7lpa7gbrealnvlb7t2vhngty

A Neuronal Classification System for Plant Leaves Using Genetic Image Segmentation

Babatunde Oluleye, Armstrong Leisa, Diepeveen Dean, Leng Jinsong
2015 British Journal of Mathematics & Computer Science  
This paper demonstrates the use of radial basis networks (RBF), cellular neural networks (CNN) and genetic algorithm (GA) for automatic classification of plant leaves.  ...  A genetic neuronal system herein attempted to solve some of the inherent challenges facing current software being employed for plant leaf classification.  ...  Acknowledgements This work was jointly supported by Edith Cowan University International Postgraduate Research Scholarship (ECUIPRS), Australia and Tertiary Educational Trust Funds (TETF) of Nigeria.  ... 
doi:10.9734/bjmcs/2015/14611 fatcat:2mhqr5wv2fh2tau24fvshthmp4

Study on Site Quality Assessment of Afforestation Land Based on GA-RBF Neural Network

Chen Yuling, Wang Chengde, Wu Baoguo and Liu Jiancheng
2019 Nature Environment and Pollution Technology  
In order to enhance the accuracy of the existing models, a new site quality assessment model based on Genetic Algorithm-Radial Basis Function (GA-RBF) was proposed to predict site index (stand dominant  ...  In this paper, the GA-RBF was compared with the radial basis function (RBF) and the traditional Quantitative Theory I (QT-I) method.  ...  A modified Genetic Algo- x (1) x (2) x (3) x ( rithm-Radial Basis Function (GA-RBF) model was applied to the study of the site quality assessment of afforestation land.  ... 
doaj:d40b880712ea43d3b7c179c2ed364ef9 fatcat:6lzeatt73fg6lnv23zzhg43a6y

A Study of Applications of RBF Network

Yojna Arora, Abhishek Singhal, Abhay Bansal
2014 International Journal of Computer Applications  
The paper includes a detailed survey on RBF network on the basis of its evolution and applications.  ...  It requires a lot of analysis on current and past outcomes in order to give timely and accurate timely forecasted results.Radial Basis Function (RBF) is a method proposed in machine learning for making  ...  Kundu, 2003, Improved K-means Algorithm in the Design of RBF Neural Networks, IEEE, VOL 2 [7] S Chen, C.F.N Cowen and P.M Grant, 1991, Orthogonal Least Squares Learning Algorithm For radial Basis Function  ... 
doi:10.5120/16315-5553 fatcat:r2vmbpthnvbddnvznhm53a3grq

Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Abdel Badie Sharkawy
2011 Applied Computational Intelligence and Soft Computing  
They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs).  ...  A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered.  ...  radial basis function neural networks (RBFNs), and so forth.  ... 
doi:10.1155/2011/183764 fatcat:uatm2ezcnzbupfkptisb5jo2y4

Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey

Gayathri B.M, Sumathi C.P, Santhanam T
2013 International Journal of Distributed and Parallel systems  
This paper summarizes the survey on breast cancer diagnosis using various machine learning algorithms and methods, which are used to improve the accuracy of predicting cancer.  ...  Waiting for diagnosing a breast cancer for a long time may increase the possibility of the cancer spreading.  ...  Radial basis function(RBF), General Regression Neural Network(GRNN),Probabilistic Neural Network(PNN) were used for classification and their overall performance were 96.18% for Radial Basis Function (RBF  ... 
doi:10.5121/ijdps.2013.4309 fatcat:kfvydgzyqnhlnfj2p7s53ba6zu

Hybrid Artificial Neural Networks: Models, Algorithms and Data [chapter]

P. A. Gutiérrez, C. Hervás-Martínez
2011 Lecture Notes in Computer Science  
Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems.  ...  In this paper, a review of some of the main contributions for hybrid ANNs is given, considering three points of views: models, algorithms and data.  ...  One possibility is to consider functions located in subspaces of the input space, such as Radial Basis Functions, RBFs, which constitute RBF neural networks [4, 42] .  ... 
doi:10.1007/978-3-642-21498-1_23 fatcat:l3huh7hohjcqvokqgp55tg67u4

Credit evaluation model of loan proposals for Indian Banks

Seema Purohit, Anjali Kulkarni
2011 2011 World Congress on Information and Communication Technologies  
The integrated model is a combination model based on the techniques of Logistic Regression, Multilayer Perceptron Model, Radial Basis Neural Network, Support Vector Machine and Decision tree (C4.5) and  ...  The failure and success of the Banking Industry depends largely on industry's ability to properly evaluate credit risk.  ...  A.  ... 
doi:10.1109/wict.2011.6141362 fatcat:7x6k4ndbnrd7lculfacezbwo4m
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