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Relevant bibliographies by topics / RBF neural networks

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Academic literature on the topic 'RBF neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "RBF neural networks"

1

Sohn, I., and N. Ansari. "Configuring RBF neural networks." Electronics Letters 34, no. 7 (1998): 684. http://dx.doi.org/10.1049/el:19980469.

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Zhu, Jian Min, Peng Du, and Ting Ting Fu. "Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods." Advanced Materials Research 317-319 (August 2011): 1529–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1529.

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The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem,

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Soper, Daniel S. "Using an Opportunity Matrix to Select Centers for RBF Neural Networks." Algorithms 16, no. 10 (2023): 455. http://dx.doi.org/10.3390/a16100455.

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When designed correctly, radial basis function (RBF) neural networks can approximate mathematical functions to any arbitrary degree of precision. Multilayer perceptron (MLP) neural networks are also universal function approximators, but RBF neural networks can often be trained several orders of magnitude more quickly than an MLP network with an equivalent level of function approximation capability. The primary challenge with designing a high-quality RBF neural network is selecting the best values for the network’s “centers”, which can be thought of as geometric locations within the input space

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Loss, D. V., M. C. F. DeCastro, P. R. G. Franco, and F. C. C. DeCastro. "Phase transmittance RBF neural networks." Electronics Letters 43, no. 16 (2007): 882. http://dx.doi.org/10.1049/el:20070016.

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Zhang, Liu. "Research of Automotive Glass Fog System Based on RBF Neural Network." Advanced Materials Research 588-589 (November 2012): 1441–45. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1441.

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By describing a danger from driving vehicles with fog on windshield, we give a concept of a new type of automatic windshield defogging system applying traditional sensor and RBF neural networks. In terms of an analysis on the source of fogging on automatic windshield, applying traditional sensor, we design a RBF neural networks. Then, via RBF neural networks mode, training and testing 48 series of data from an experiment. A result of MATLAB software demonstrates that this new system defog from automatic windshield swiftly and precisely by applying RBF neural networks.

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Zhang, Min-Ling. "Ml-rbf: RBF Neural Networks for Multi-Label Learning." Neural Processing Letters 29, no. 2 (2009): 61–74. http://dx.doi.org/10.1007/s11063-009-9095-3.

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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 (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

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Schmitt, Michael. "Descartes' Rule of Signs for Radial Basis Function Neural Networks." Neural Computation 14, no. 12 (2002): 2997–3011. http://dx.doi.org/10.1162/089976602760805386.

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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

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Liu, Dong Dong. "A Method about Load Distribution of Rolling Mills Based on RBF Neural Network." Advanced Materials Research 279 (July 2011): 418–22. http://dx.doi.org/10.4028/www.scientific.net/amr.279.418.

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Rolling mills process is too complicated to be described by formulas. RBF neural networks can establish finishing thickness and rolling force models. Traditional models are still useful to the neural network output. Compared with those finishing models which have or do not have traditional models as input, the importance of traditional models in application of neural networks is obvious. For improving the predictive precision, BP and RBF neural networks are established, and the result indicates that the model of load distribution based on RBF neural network is more accurate.

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Shabaninia, Faridoon, Mehdi Roopaei, and Mehdi Fatemi. "Post-training on RBF neural networks." Nonlinear Analysis: Hybrid Systems 1, no. 4 (2007): 491–500. http://dx.doi.org/10.1016/j.nahs.2005.11.003.

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Dissertations / Theses on the topic "RBF neural networks"

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Li, Junxu. "A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks." Fogler Library, University of Maine, 1999. http://www.library.umaine.edu/theses/pdf/LiJ1999.pdf.

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Medagam, Peda Vasanta Reddy. "Online optimal control for a class of nonlinear system using RBF neural networks /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650508351&sid=19&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all s

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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all s

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Machado, Madson Cruz. "Sintonia RNA-RBF para o Projeto Online de Sistemas de Controle Adaptativo." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1744.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-07-18T19:31:22Z No. of bitstreams: 1 MadsonMachado.pdf: 3046442 bytes, checksum: 71cc6800f83fdbf38b97607067653f63 (MD5)<br>Made available in DSpace on 2017-07-18T19:31:22Z (GMT). No. of bitstreams: 1 MadsonMachado.pdf: 3046442 bytes, checksum: 71cc6800f83fdbf38b97607067653f63 (MD5) Previous issue date: 2017-05-26<br>The need to increase industrial productivity coupled with quality and low cost requirements has generated a demand for the development of high performance controllers. Motivated by this demand, we presented in this work

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Selmini, Antonio Marcos. "Aplicação de redes neurais artificiais e filtro de Kalman para redução de ruídos em sinais de voz." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-29072016-111821/.

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A filtragem, na sua forma mais geral, tem estado presente na vida do homem há muito tempo. Com o surgimento de novas tecnologias (surgimento da eletricidade e a sua evolução) e o desenvolvimento da computação, as técnicas de filtragem (separação) de sinais elétricos. Normalmente, os sistemas de comunicação (telefonia móvel e fixa, sinais recebidos de satélites e outros sistemas) contém sinais indesejáveis responsáveis pela degradação do sinal original. Dentro desse contexto, este projeto de pesquisa apresenta um estudo do algoritmo Filtro Duplo de Kalman Estendido, onde um filtro e Kalman e du

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Bassi, Regiane Denise Solgon. "Identicação inteligente de patologias no trato vocal." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-14032014-080118/.

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Com base em exames como a videolaringoscopia, que é considerado um procedimento médico invasivo e desconfortável, diagnósticos têmsido realizados visando detectar patologias na laringe. Geralmente, esse tipo de exame é realizado somente com solicitação médica e quando alterações na fala já são marcantes, ou há sensação de dor. Nessa fase, muitas vezes a doença está em grau avançado, dificultando o seu tratamento. Com o objetivo de realizar um pré-diagnóstico computacional de tais patologias, este trabalho apresenta uma técnica não invasiva na qual são testados e comparados três classificadores

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Nawathe, Piyush. "Neural Network Trees and Simulation Databases: New Approaches for Signalized Intersection Crash Classification and Prediction." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4067.

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Intersection related crashes form a significant proportion of the crashes occurring on roadways. Many organizations such as the Federal Highway Administration (FHWA) and American Association of State Highway and Transportation Officials (AASHTO) are considering intersection safety improvement as one of their top priority areas. This study contributes to the area of safety of signalized intersections by identifying the traffic and geometric characteristics that affect the different types of crashes. The first phase of this thesis was to classify the crashes occurring at signalized intersections

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Bosack, Matthew James. "Magnetic Signature Estimation Using Neural Networks." Master's thesis, Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/178597.

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Electrical Engineering<br>M.S.E.E.<br>Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are gen

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Thakkar, Pinal. "NEURAL NETWORKS SATISFYING STONE-WEIESTRASS THEOREM AND APPROXIMATING." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4060.

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Neural networks are an attempt to build computer networks called artificial neurons, which imitate the activities of the human brain. Its origin dates back to 1943 when neurophysiologist Warren Me Cello and logician Walter Pits produced the first artificial neuron. Since then there has been tremendous development of neural networks and their applications to pattern and optical character recognition, speech processing, time series prediction, image processing and scattered data approximation. Since it has been shown that neural nets can approximate all but pathological functions, Neil Cotter co

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Books on the topic "RBF neural networks"

1

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

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

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Hong, X. A Givens rotation based fast backward elimination algorithm for RBF neural network pruning. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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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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5

Luppi, Pierre-Hervé, Olivier Clément, Christelle Peyron, and Patrice Fort. Neurobiology of REM sleep. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0003.

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REM (paradoxical) sleep is a state characterized by rapid eye movements, EEG activation, and muscle atonia. REM sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia during REM sleep. Cataplexy, a key symptom of narcolepsy, is a striking sudden episode of muscle weakness comparable to REM sleep atonia triggered by emotions during wakefulness. This chapter presents recent results on the neuronal network responsible for REM sleep and explores hypotheses explaining RBD and cataplexy. RBD could be due to a specific degeneration of glutamatergic neurons responsible fo

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Göknar, Izzet, and Levent Sevgi. Complex Computing-Networks: Brain-Like and Wave-oriented Electrodynamic Algorithms. Springer London, Limited, 2006.

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(Editor), I. C. Göknar, and L. Sevgi (Editor), eds. Complex Computing-Networks : Brain-like and Wave-oriented Electrodynamic Algorithms (Springer Proceedings in Physics) (Springer Proceedings in Physics). Springer, 2006.

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Book chapters on the topic "RBF neural networks"

1

Rivas, V. M., P. A. Castillo, and J. J. Merelo. "Evolving RBF Neural Networks." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_60.

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Rafajłowicz, Ewaryst. "Extended RBF Nets — Preliminary Studies." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_36.

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Nagabhushan, T. N., and S. K. Padma. "Adaptive Learning in Incremental Learning RBF Networks." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_72.

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da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. "Performance Analysis of RBF and MLP Networks in Pattern Classification." In Artificial Neural Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_19.

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Kecman, Vojislav, and Zhenquan Li. "Fuzzy Calculus by RBF Neural Networks." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_79.

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Roberts, Stephen, and Lionel Tarassenko. "Automated Sleep EEg Analysis using an RBF Network." In Applications of Neural Networks. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2379-3_13.

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Demian, V., F. Desprez, H. Paugam-Moisy, and M. Pourzandi. "Parallel implementation of RBF neural networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0024708.

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Buchtala, Oliver, Alexander Hofmann, and Bernhard Sick. "Fast and Efficient Training of RBF Networks." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_6.

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Li, Michael, and Santoso Wibowo. "Bayesian Curve Fitting Based on RBF Neural Networks." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_13.

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Constantinopoulos, Constantinos, and Aristidis Likas. "Active Learning with the Probabilistic RBF Classifier." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_38.

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Conference papers on the topic "RBF neural networks"

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Zhang, Mengqi, Matthias Treder, David Marshall, and Yuhua Li. "Fast Explanation of RBF-Kernel SVM Models Using Activation Patterns." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650697.

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He, Tao, Yinbing Wang, Zhicheng Lin, Kai Guo, Duo Li, and Tao Huang. "RBF Neural Networks Model-Free Feedforward Control of Piezoelectric Actuator." In 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE). IEEE, 2024. http://dx.doi.org/10.1109/cacre62362.2024.10634870.

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Papadopoulos, Spyridon K., Teo Protoulis, and Alex Alexandridis. "Quadcopter Attitude Control using Nonlinear MPC and RBF Neural Networks." In 2025 33rd Mediterranean Conference on Control and Automation (MED). IEEE, 2025. https://doi.org/10.1109/med64031.2025.11073520.

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Khan, Shujaat, Jawwad Ahmad, Alishba Sadiq, Imran Naseem, and Muhammad Moinuddin. "Spatio-Temporal RBF Neural Networks." In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE, 2018. http://dx.doi.org/10.1109/iceest.2018.8643322.

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Bellocchio, Francesco, Stefano Ferrari, Vincenzo Piuri, and N. Alberto Borghese. "Online training of Hierarchical RBF." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371292.

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Da, Lianglong, Guangzhi Shi, Junchuan Hu, and Yuyang Li. "A RBF neural networks based feature." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554312.

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Wei, Haikun, and Shun-ichi Amari. "Eigenvalue Analysis on Singularity in RBF networks." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371040.

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Townsend, N. W. "Estimations of error bounds for RBF networks." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970731.

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Huang, Bai-Gang, and Zao-Jian Zou. "Short-Term Prediction of Ship Pitching Motion Based on Artificial Neural Networks." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54317.

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Due to the random nature of ship motion in an open sea environment, the ship related maritime operations such as landing on an aircraft carrier, ship-borne helicopter recovery, cargo transfer between ships and so on, are usually very difficult. An accurate prediction of the motion will improve the operation safety and efficiency on board ships. This paper presents a research on the application of artificial neural network methods in the short-time prediction of ship pitching motion. The radial basis function (RBF) neural network is applied to develop a model for short-time prediction of ship p

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"Fault Identification and Diagnosis in Air Compressor Systems using RBF Neural Networks." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/mbvt0954.

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Fault identification and diagnosis in air compressor systems are critical for maintaining operational efficiency, reducing downtime, and minimizing maintenance costs. Traditional diagnostic methods often struggle with the complexity and variability of faults in these systems. This research paper explores the use of Radial Basis Function (RBF) neural networks for the identification and diagnosis of faults in air compressor systems. RBF neural networks, known for their powerful pattern recognition capabilities, are employed to enhance the accuracy and reliability of fault detection. The study de

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Reports on the topic "RBF neural networks"

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature

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