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Relevant bibliographies by topics / Radial Basis Function Neural Network (RBFN)

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Academic literature on the topic 'Radial Basis Function Neural Network (RBFN)'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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Journal articles on the topic "Radial Basis Function Neural Network (RBFN)"

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Babu, N. S. C., and V. C. Prasad. "Radial Basis Function Networks for Analog Circuit Fault Isolation." Journal of Circuits, Systems and Computers 07, no. 06 (December 1997): 643–55. http://dx.doi.org/10.1142/s0218126697000462.

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The application of a radial basis function neural network (RBFN) for analog circuit fault isolation is presented. In this method the RBFN replaces the fault dictionary of analog circuits. The proposed method for analog circuit fault isolation takes the advantage of extremely fast training of RBFN compared to earlier neural network methods. A method is suggested to select centers and widths of RBF units. This selection procedure accounts for the component tolerances. The effectiveness of the RBFN for the fault isolation problem is demonstrated with an illustrative example. RBFN performed well even when the input patterns are drawn directly from the test node voltages of the analog circuit under consideration. A method is suggested to modify the RBF network in the event of occurrence of a new fault. The suggested modifications do not affect the previous training.

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HUANG, DE-SHUANG. "RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1083–101. http://dx.doi.org/10.1142/s0218001499000604.

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This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.

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Krishnamoorthy, Vinoth Kumar, Usha Nandini Duraisamy, Amruta S. Jondhale, Jaime Lloret, and Balaji Venkatesalu Ramasamy. "Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network." International Journal of Distributed Sensor Networks 2023 (March 30, 2023): 1–12. http://dx.doi.org/10.1155/2023/1426430.

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The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.

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Safavi, A., M. H. Esteki, S. M. Mirvakili, and M. Khaki. "Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation." Kerntechnik 85, no. 1 (December 1, 2020): 15–25. http://dx.doi.org/10.1515/kern-2020-850105.

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Abstract Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Radial basis function neural networks: a topical state-of-the-art survey." Open Computer Science 6, no. 1 (May 2, 2016): 33–63. http://dx.doi.org/10.1515/comp-2016-0005.

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AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 2 (June 1, 2019): 120. http://dx.doi.org/10.11591/ijai.v8.i2.pp120-131.

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In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.

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Ibrahim, Ashraf Osman, Walaa Akif Hussien, Ayat Mohammoud Yagoop, and Mohd Arfian Ismail. "Feature Selection and Radial Basis Function Network for Parkinson Disease Classiﬁcation." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 167–71. http://dx.doi.org/10.24017/science.2017.3.121.

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Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Parkinson’s disease. The feature selection (FS) method used to reduce the number of attributes in Parkinson disease data. The Parkinson disease dataset is acquired from UCI repository of large well-known data sets. The experimental results have revealed significant improvement to detect Parkinson’s disease using feature selection method and RBF network.

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Ding, Shuo, and Xiao Heng Chang. "A MATLAB-Based Study on the Realization and Approximation Performance of RBF Neural Networks." Applied Mechanics and Materials 325-326 (June 2013): 1746–49. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1746.

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BP neural network is a kind of widely used feed-forward network. However its innate shortcomings are gradually giving rise to the study of other networks. Currently one of the research focuses in the area of feed-forward networks is radial basis function neural network. To test the radial basis function neural network for nonlinear function approximation capability, this paper first introduces the theories of RBF networks, as well as the structure, function approximation and learning algorithm of radial basis function neural network. Then a simulation test is carried out to compare BPNN and RBFNN. The simulation results indicate that RBFNN is simpler in structure, faster in speed and better in approximation performance. That is to say RBFNN is superior to BPNN in many aspects. But when solving the same problem, the structure of radial basis networks is more complicated than that of BP neural networks. Keywords: Radial basis function; Neural network; Function approximation; Simulation; MATLAB

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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "An Improved Radial Basis Function Networks in Networks Weights Adjustment for Training Real-World Nonlinear Datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 63. http://dx.doi.org/10.11591/ijai.v8.i1.pp63-76.

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In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.

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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 54. http://dx.doi.org/10.11591/ijai.v8.i1.pp54-62.

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The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.

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Dissertations / Theses on the topic "Radial Basis Function Neural Network (RBFN)"

1

Guo, Zhihao. "Intelligent multiple objective proactive routing in MANET with predictions on delay, energy, and link lifetime." online version, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1195705509.

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Murphy, Ethan Kane. "Radial-Basis-Function Neural Network Optimization of Microwave Systems." Digital WPI, 2003. https://digitalcommons.wpi.edu/etd-theses/77.

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An original approach in microwave optimization, namely, a neural network procedure combined with the full-wave 3D electromagnetic simulator QuickWave-3D implemented a conformal FDTD method, is presented. The radial-basis-function network is trained by simulated frequency characteristics of S-parameters and geometric data of the corresponding system. High accuracy and computational efficiency of the procedure is illustrated for a waveguide bend, waveguide T-junction with a post, and a slotted waveguide as a radiating element.

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Murphy, Ethan Kane. "Radial-Basis-Function Neural Network Optimization of Microwave Systems." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0113103-121206/.

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Master's Project (M.S.) -- Worcester Polytechnic Institute.
Keywords: optimization technique; microwave systems; optimization technique; neural networks; QuickWave 3D. Includes bibliographical references (p. 68-71).

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Aguilar, David P. "A radial basis neural network for the analysis of transportation data." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000515.

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Fathala, Giuma Musbah. "Analysis and implementation of radial basis function neural network for controlling non-linear dynamical systems." Thesis, University of Newcastle upon Tyne, 1998. http://hdl.handle.net/10443/3114.

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Modelling and control of non-linear systems are not easy, which are now being solved by the application of neural networks. Neural networks have been proved to solve these problems as they are described by adjustable parameters which are readily adaptable online. Many types of neural networks have been used and the most common one is the backpropagation algorithm. The algorithm has some disadvantages, such as slow convergence and construction complexity. An alternative neural networks to overcome the limitations associated with the backpropagation algorithm is the Radial Basis Function Network which has been widely used for solving many complex problems. The Radial Basis Function Network is considered in this theses, along with a new adaptive algorithm which has been developed to overcome the problem of the optimum parameter selection. Use of the new algorithm reduces the trial and error of selecting the minimum required number of centres and guarantees the optimum values of the centres, the widths between the centres and the network weights. Computer simulation usmg SimulinklMatlab packages, demonstrated the results of modelling and control of non-linear systems. Moreover, the algorithm is used for selecting the optimum parameters of a non-linear real system 'Brushless DC Motor'. In the laboratory implementation satisfactory results have been achieved, which show that the Radial Basis Function may be used for modelling and on-line control of such real non-linear systems.

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Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.

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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-layer perceptron networks (MLPNs) and radial basis function networks (RBFNs) to effectively eliminate the volume integrals in the integral formulation of fluid flow problems. An adaptive velocity gradient domain decomposition (AVGDD) scheme is incorporated into the computational algorithm. As a result, an improved feed forward neural network boundary-element-only method (FFNN- BEM) is created and verified. The present FFNN-BEM successfully simulates the flow of several Generalised Newtonian Fluids (GNFs), including the Carreau, Power-law and Cross models. To the best of the author's knowledge, the present FFNN-BEM is the first to achieve convergence for difficult flow situations when the power-law indices are very small (as small as 0.2). Although some elements are still used to discretise the governing equations, but only on the boundary of the analysis domain, the experience gained in the development of element-free approximation in the domain provides valuable skills for the progress towards an element-free approach. A least squares collocation RBFN-based mesh-free method is then developed for solving the governing PDEs. This method is coupled with the stochastic simulation technique (SST), forming the mesoscopic approach for analyzing viscoelastic flid flows. The velocity field is computed from the RBFN-based mesh-free method (macroscopic component) and the stress is determined by the SST (microscopic component). Thus the SST removes a limitation in traditional macroscopic approaches since closed form constitutive equations are not necessary in the SST. In this mesh-free method, each of the unknowns in the conservation equations is represented by a linear combination of weighted radial basis functions and hence the unknowns are converted from physical variables (e.g. velocity, stresses, etc) into network weights through the application of the general linear least squares principle and point collocation procedure. Depending on the type of RBFs used, a number of parameters will influence the performance of the method. These parameters include the centres in the case of thin plate spline RBFNs (TPS-RBFNs), and the centres and the widths in the case of multi-quadric RBFNs (MQ-RBFNs). A further improvement of the approach is achieved when the Eulerian SST is formulated via Brownian configuration fields (BCF) in place of the Lagrangian SST. The SST is made more efficient with the inclusion of the control variate variance reduction scheme, which allows for a reduction of the number of dumbbells used to model the fluid. A highly parallelised algorithm, at both macro and micro levels, incorporating a domain decomposition technique, is implemented to handle larger problems. The approach is verified and used to simulate the flow of several model dilute polymeric fluids (the Hookean, FENE and FENE-P models) in simple as well as non-trivial geometries, including shear flows (transient Couette, Poiseuille flows)), elongational flows (4:1 and 10:1 abrupt contraction flows) and lid-driven cavity flows.

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Lee, Hee Eun. "Hierarchical modeling of multi-scale dynamical systems using adaptive radial basis function neural networks: application to synthetic jet actuator wing." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/230.

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To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.

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Kattekola, Sravanthi. "Weather Radar image Based Forecasting using Joint Series Prediction." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1238.

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Accurate rainfall forecasting using weather radar imagery has always been a crucial and predominant task in the field of meteorology [1], [2], [3] and [4]. Competitive Radial Basis Function Neural Networks (CRBFNN) [5] is one of the methods used for weather radar image based forecasting. Recently, an alternative CRBFNN based approach [6] was introduced to model the precipitation events. The difference between the techniques presented in [5] and [6] is in the approach used to model the rainfall image. Overall, it was shown that the modified CRBFNN approach [6] is more computationally efficient compared to the CRBFNN approach [5]. However, both techniques [5] and [6] share the same prediction stage. In this thesis, a different GRBFNN approach is presented for forecasting Gaussian envelope parameters. The proposed method investigates the concept of parameter dependency among Gaussian envelopes. Experimental results are also presented to illustrate the advantage of parameters prediction over the independent series prediction.

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Matta, Mariel Cadena da. "Processamento de imagens em dosimetria citogenética." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/10141.

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Submitted by Amanda Silva (amanda.osilva2@ufpe.br) on 2015-03-03T14:16:54Z No. of bitstreams: 2 Dissertação Mariel Cadena da Matta.pdf: 2355898 bytes, checksum: 9c0530af680cf965137a2385d949b799 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)
Made available in DSpace on 2015-03-03T14:16:54Z (GMT). No. of bitstreams: 2 Dissertação Mariel Cadena da Matta.pdf: 2355898 bytes, checksum: 9c0530af680cf965137a2385d949b799 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013
FACEPE
A Dosimetria citogenética empregando análise de cromossomos dicêntricos é o “padrão ouro” para estimativas da dose absorvida após exposições acidentais às radiações ionizantes. Todavia, este método é laborioso e dispendioso, o que torna necessária a introdução de ferramentas computacionais que dinamizem a contagem dessas aberrações cromossômicas radioinduzidas. Os atuais softwares comerciais, utilizados no processamento de imagens em Biodosimetria, são em sua maioria onerosos e desenvolvidos em sistemas dedicados, não podendo ser adaptados para microscópios de rotina laboratorial. Neste contexto, o objetivo da pesquisa foi o desenvolvimento do software ChromoSomeClassification para processamento de imagens de metáfases de linfócitos (não irradiados e irradiados) coradas com Giemsa a 5%. A principal etapa da análise citogenética automática é a separação correta dos cromossomos do fundo, pois a execução incorreta desta fase compromete o desenvolvimento da classificação automática. Desta maneira, apresentamos uma proposta para a sua resolução baseada no aprimoramento da imagem através das técnicas de mudança do sistema de cores, subtração do background e aumento do contraste pela modificação do histograma. Assim, a segmentação por limiar global simples, seguida por operadores morfológicos e pela técnica de separação de objetos obteve uma taxa de acerto de 88,57%. Deste modo, os cromossomos foram enfileirados e contabilizados, e assim, a etapa mais laboriosa da Dosimetria citogenética foi realizada. As características extraídas dos cromossomos isolados foram armazenadas num banco de dados para que a classificação automática fosse realizada através da Rede Neural com Funções de Ativação de Base Radial (RBF). O software proposto alcançou uma taxa de sensibilidade de 76% e especificidade de 91% que podem ser aprimoradas através do acréscimo do número de objetos ao banco de dados e da extração de mais características dos cromossomos.

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Ringienė, Laura. "Hybrid neural network for multidimensional data visualization." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140912_140117-42267.

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The area of research is data mining based on multidimensional data visual analysis. This allows researcher to participate in the process of data analysis directly, to understand the complex data better and to make the best decisions. The objective of the dissertation is to create a method for making a multidimensional data projection on the plane such that the researcher could see and assess the intergroup similarities and differences of multidimensional points. In order to achieve the target, a new hybrid neural network is proposed and investigated. This neural network integrates the ideas both of the radial basis function neural network and that of a multilayer perceptron, which has the properties of a ''bottleneck'' neural network. The new network is used for the visual analysis of multidimensional data in such a way that the output values of the neurons of the last hidden layer are the two-dimensional or three-dimensional projections of the multidimensional data, when the multidimensional data is given to the network. A peculiarity of the network is that the visualization results on the plane reflect the general structure of the data (clusters, proximity between clusters, intergroup similarities of points) rather than the location of multidimensional points.
Šio darbo tyrimų sritis yra duomenų tyryba remiantis daugiamačių duomenų vizualia analize. Tai leidžia tyrėjui betarpiškai dalyvauti duomenų analizės procese, geriau pažinti sudėtingus duomenis ir priimti geriausius sprendimus. Disertacijos tikslas yra sukurti metodą tokios duomenų projekcijos radimui plokštumoje, kad tyrėjas galėtų pamatyti ir įvertinti daugiamačių taškų tarpgrupinius panašumus/skirtingumus. Šiam tikslui pasiekti yra pasiūlytas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono, turinčio ,,butelio kaklelio“ neuroninio tinklo savybes, junginys. Naujas tinklas naudojamas vizualiai daugiamačių duomenų analizei, kai atidėjimui plokštumoje arba trimatėje erdvėje taškai gaunami paskutinio paslėpto neuronų sluoksnio išėjimuose, kai į tinklo įėjimą paduodami daugiamačiai duomenys. Šio tinklo ypatybė yra ta, kad gautas vaizdas plokštumoje labiau atspindi bendrą duomenų struktūrą (klasteriai, klasterių tarpusavio artumas, taškų tarpklasterinis panašumas) nei daugiamačių taškų tarpusavio išsidėstymą.

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Books on the topic "Radial Basis Function Neural Network (RBFN)"

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34816-7.

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Radial Basis Function Rbf Neural Network Control For Mechanical Systems Design Analysis And Matlab Simulation. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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A Radial Basis Function Neural Network Approach to Two-Color Infrared Missile Detection. Storming Media, 2001.

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Liu, Jinkun. Radial Basis Function Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Springer Berlin / Heidelberg, 2015.

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Book chapters on the topic "Radial Basis Function Neural Network (RBFN)"

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Sundararajan, N., P. Saratchandran, and Yan Li. "Indirect Adaptive Control Using Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 69–80. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_4.

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Liu, Jinkun. "Discrete Neural Network Control." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 311–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_10.

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Liu, Jinkun. "Adaptive RBF Neural Network Control." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 71–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_4.

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Liu, Jinkun. "Digital RBF Neural Network Control." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 293–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_9.

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Sundararajan, N., P. Saratchandran, and Yan Li. "Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 29–45. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_2.

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Sundararajan, N., P. Saratchandran, and Yan Li. "Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 85–94. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_5.

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Liu, Jinkun. "Neural Network Sliding Mode Control." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 113–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_5.

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Liu, Jinkun. "RBF Neural Network Design and Simulation." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 19–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_2.

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Liu, Jinkun. "RBF Neural Network Control Based on Gradient Descent Algorithm." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 55–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_3.

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Liu, Jinkun. "Backstepping Control with RBF." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 251–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_8.

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Conference papers on the topic "Radial Basis Function Neural Network (RBFN)"

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Luo, Run, Shifa Wu, Xinyu Wei, and Fuyu Zhao. "Identification Modeling of Accelerator Driven System Based on Growing and Pruning Radial Basis Function Network." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60328.

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An identification method based on growing and pruning radial basis function network (GAP-RBFN) is presented for modeling an accelerator driven system (ADS). Compared with traditional neural networks, GAP-RBFN could automatically adjust the number of hidden neurons to find a suitable network structure by using growing and pruning strategies. In addition, an extended Kalman filter (EKF) algorithm is adopted to update network parameters of neurons in GAP-RBFN, which has a rapid convergence speed during the training process. A numerical calculation code named ARTAP (ADS Reactor Transient Analysis Program) is used to generate data for training GAP-RBFN. After GAP-RBFN is trained by the data, an identification model for ADS is established. The simulation results obtained from the GAP-RBFN model are compared with those obtained from a recurrent neural network (RNN) model. It is shown that the GAP-RBFN model not only has higher prediction accuracy than the RNN model, but also has faster computation speed than the numerical calculation code. Owing to its accuracy, simplicity and fast computation speed, the proposed GAP-RBFN method can be used to model the ADS reactor.

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Narayanan, Madusudanan Sathia, Puneet Singla, Sudha Garimella, Wayne Waz, and Venkat Krovi. "Radial Basis Function Network (RBFN) Approximation of Finite Element Models for Real-Time Simulation." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6154.

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Nonlinearities inherent in soft-tissue interactions create roadblocks to realization of high-fidelity real-time haptics-based medical simulations. While finite element (FE) formulations offer greater accuracy over conventional spring-mass-network models, computational-complexity limits achievable simulation-update rates. Direct interaction with sensorized physical surrogates, in offline or online modes, allows a temporary sidestepping of computational issues but hinders parametric analysis and true exploitation of a simulation-based testing paradigm. Hence, in this paper, we develop Radial-Basis Neural-Network approximations, to FE-model data within a Modified Resource Allocating Network (MRAN) framework. Real-time simulation of the reduced order neural-network approximations at high temporal resolution provided the haptic-feedback. Validation studies are being conducted to evaluate the kinesthetic realism of these models with medical experts.

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Milovanovic, Ivana. "Radial Basis Function (RBF) networks for improved gait analysis." In 2008 9th Symposium on Neural Network Applications in Electrical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/neurel.2008.4685588.

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Sui, Wenbo, and Carrie M. Hall. "SCR Control System Design Based on On-Line Radial Basis Function and Backpropagation Neural Networks." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5095.

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Because of its high NOx reduction efficiency, selective catalyst reduction (SCR) has become an indispensable part of diesel vehicle aftertreatment. This paper presents a control strategy for SCR systems that is based on an on-line radial basis function neural network (RBFNN) and an on-line backpropagation neural network (BPNN). In this control structure, the radial basis function neural network is employed as an estimator to provide Jacobian information for the controller; and the backpropagation neural network is utilized as a controller, which dictates the appropriate urea-solution to be injected into the SCR system. This design is tested by simulations based in Gamma Technologies software (GT-ISE) as well as MATLAB Simulink. The results show that the RBF-BPNN control technique achieves a 1–5 % higher NOx reduction efficiency than a PID controller.

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Boopathi, G., and S. Arockiasamy. "Image compression: Wavelet transform using radial basis function (RBF) neural network." In 2012 Annual IEEE India Conference (INDICON). IEEE, 2012. http://dx.doi.org/10.1109/indcon.2012.6420640.

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Syafaruddin, Salama Manjang, and Satriani Latief. "Radial basis function (RBF) neural network for load forecasting during holiday." In 2016 3rd Conference on Power Engineering and Renewable Energy (ICPERE). IEEE, 2016. http://dx.doi.org/10.1109/icpere.2016.7904869.

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Arakawa, Masao, Hirotaka Nakayama, and Hiroshi Ishikawa. "Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8637.

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Abstract In this paper, we use the radial basis function networks in order to approximate the fitness function of the genetic algorithms and try to obtain the approximate optimum results within the relatively small number of function call. The radial basis function network (RBF) is a kind of neural network that is composed by the number of radial basis function in Gaussian distribution. Its learning system is composed by additional learning of basis function and a new data and forgetting of basis function and undesirable data. Thus the key issues in RBF are to give new data and to place basis function. So that if we can give these values appropriately, we can carry out approximate optimization even in the case that the optimum solutions are outside the range of the initial settings. Together with the adaptive range genetic algorithms that are proposed to treat mixed variable optimization, we will propose the way to give a new data and basis function. In this study, we have shown the effectiveness of the proposed method through simple numerical examples.

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Yunan, Izzuddin, Ihsan M. Yassin, Syed Farid Syed Adnan, and Mohd Hezri Fazalul Rahiman. "Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network." In 2012 IEEE 8th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2012. http://dx.doi.org/10.1109/cspa.2012.6194779.

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GAO, MINGLIANG, SHAN GAO, CHUANG YU, DEQUAN LI, SHIJI SONG, HAIMING SHI, HONGLIANG SUN, and HONGCHAO WANG. "RESEARCH AND APPLICATION OF RADIAL BASIS NETWORK BOGIE FAULT DIAGNOSIS MODEL BASED ON PARTICLE SWARM OPTIMIZATION." In 3rd International Workshop on Structural Health Monitoring for Railway System (IWSHM-RS 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/iwshm-rs2021/36030.

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Bogie system is the key system that affects the safety and quality of EMU operation. The construction of fault diagnosis model for bogie system can effectively improve the safety and comfort of EMU operation. The traditional modeling method uses BP neural network to model by fitting bogie system temperature and other parameters. However, BP neural network is prone to fall into local minimum, slow convergence and poor diagnostic accuracy. In this paper, particle radial basis function neural network (PSRB) is designed by using particle swarm optimization algorithm with high convergence. Particle Swarm optimization (PSO) is used to optimize the parameters of RBF Neural Networks. According to the complexity of the input parameters of the bogie system, the input and output parameters of the model are determined. Particle swarm optimization algorithm is used to search the optimal values of the center, width and output layer weight threshold of the RBF neural network. The hybrid algorithm is applied to the fault diagnosis of bogie system, and a bogie fault diagnosis model based on particle radial basis function neural network is designed. The experimental results show that the diagnosis model can effectively improve the identification accuracy of fault diagnosis, the minimum error accuracy is 0.0055, the operation time is saved, the operation time is reduced to 1.9s, and the influence of non-target parameters on the inversion results is eliminated. The model can also be used in other EMU systems, and has practical application value.

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Roh, Young Jun, and Hyungsuck Cho. "Image reconstruction in x-ray tomography using a radial basis function (RBF) neural network." In Intelligent Systems and Advanced Manufacturing, edited by Hyungsuck Cho. SPIE, 2001. http://dx.doi.org/10.1117/12.444110.

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