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Journal articles on the topic 'RBF neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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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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3

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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5

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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6

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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8

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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9

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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11

Ros, Frédéric, Marco Pintore, Arnaud Deman, and Jacques R. Chrétien. "Automatical initialization of RBF neural networks." Chemometrics and Intelligent Laboratory Systems 87, no. 1 (2007): 26–32. http://dx.doi.org/10.1016/j.chemolab.2006.01.008.

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12

Wurzberger, Fabian, and Friedhelm Schwenker. "Learning in Deep Radial Basis Function Networks." Entropy 26, no. 5 (2024): 368. http://dx.doi.org/10.3390/e26050368.

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Learning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-layered RBF networks are very well established; therefore, deeper architectures are theoretically not required. Consequently, RBFs are mostly used in a single-layered manner. However, deep neural networks have proven their effectiveness on many different tasks. In this paper, we show that deeper RBF architectures with multiple radial basis function la

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13

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 (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 e

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Wang, Wu, and Zheng Yin Zhao. "Application of Adaptive RBF-SMC for Electro-Hydraulic Position Servo System." Advanced Materials Research 463-464 (February 2012): 1440–44. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1440.

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Electro-hydraulic servo system was hard to control with traditional control strategy and RBF-SMC (Radial Basis Function neural networks-Sliding Mode Control) controller was designed for this system. The mathematical model of the electro-hydraulic servo system was analyzed and the neural sliding mode controller was designed, the control law of sliding mode control was based on linearization feedback techniques and estimate parameters with RBF neural network. The simulation shows RBF neural networks can learning the uncertainties and disturbance, RBF-SMC has good control performance of reduces c

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Tsoulos, Ioannis G., Alexandros Tzallas, and Evangelos Karvounis. "A Two-Phase Evolutionary Method to Train RBF Networks." Applied Sciences 12, no. 5 (2022): 2439. http://dx.doi.org/10.3390/app12052439.

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This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.

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Aftab, Wasim, Muhammad Moinuddin, and Muhammad Shafique Shaikh. "A Novel Kernel for RBF Based Neural Networks." Abstract and Applied Analysis 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/176253.

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Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron’s center, and so forth. In this work, we introduce a novel RBF artificial neural network (ANN) where the basis function utilizes a linear combination of ED based Gaussian kernel and a cosine kernel where the cosine kernel computes the angle between feature and center vectors. Novelty of the proposed work relies

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17

Su, Hong Sheng. "Stream Turbine Vibration Fault Diagnosis." Applied Mechanics and Materials 340 (July 2013): 90–94. http://dx.doi.org/10.4028/www.scientific.net/amm.340.90.

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RBF neural networks possessed the excellent characteristics such as insensitive on the initial weights and parameters with artificial fish-swarm algorithm (AFSA) applied, which made it have abilities to get rid of the local extremum and obtain the global extremum, and called as AFSA-RBF neural networks. In this paper, a new stream turbine vibration fault diagnosis method was presented based on AFSA-RBF neural networks. After quantification and reduction of the diagnosis decision table, the simplified decision table served as the learning samples of AFSA-RBF neural network, and the well-trained

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18

Wen, Hui, Tao Yan, Zhiqiang Liu, and Deli Chen. "Integrated neural network model with pre-RBF kernels." Science Progress 104, no. 3 (2021): 003685042110261. http://dx.doi.org/10.1177/00368504211026111.

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To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an

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Luan, Tiantian, Mingxiao Sun, Guoqing Xia, and Daidai Chen. "Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate." Complexity 2018 (October 22, 2018): 1–19. http://dx.doi.org/10.1155/2018/6950124.

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The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidd

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20

Shymkovych, Volodymyr, Sergii Telenyk, and Petro Kravets. "Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA." Neural Computing and Applications 33, no. 15 (2021): 9467–79. http://dx.doi.org/10.1007/s00521-021-05706-3.

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AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixe

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21

Kowalski, C. T., and M. Kaminski. "Rotor fault detector of the converter-fed induction motor based on RBF neural network." Bulletin of the Polish Academy of Sciences: Technical Sciences 62, no. 1 (2014): 69–76. http://dx.doi.org/10.2478/bpasts-2014-0008.

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Abstract This paper deals with the application of the Radial Basis Function (RBF) networks for the induction motor fault detection. The rotor faults are analysed and fault symptoms are described. Next the main stages of the design methodology of the RBF-based neural detectors are described. These networks are trained and tested using measurement data of the stator current (MCSA). The efficiency of developed RBF-NN detectors is evaluated. Furthermore, influence of neural networks complexity and parameters of the RBF activation function on the quality of data classification is shown. The present

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22

Bandari, Madhu, and P. Pavan Kumar. "Securing Iot Networks Against Fraud Using Deep Radial Basis Function Neural Networks." Journal of Neonatal Surgery 14, no. 5 (2025): 219–26. https://doi.org/10.52783/jns.v14.2926.

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The rapid proliferation of Internet of Things (IoT) devices has led to an increased risk of security frauds within IoT networks. Traditional security measures often fall short in addressing the dynamic and diverse nature of these frauds. The heterogeneity of IoT devices and their intricate communication patterns pose significant challenges in identifying potential security breaches. Conventional security approaches struggle to adapt to the evolving fraud landscape, necessitating the exploration of advanced techniques. Deep Radial Basis Function (RBF) networks offer promise in capturing the com

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23

Grabusts, Peter. "EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 18, 2005): 33. http://dx.doi.org/10.17770/etr2005vol1.2128.

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This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.

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Ma, Lili, Jiangping Liu, and Jidong Luo. "Method of Wireless Sensor Network Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 09 (2017): 114. http://dx.doi.org/10.3991/ijoe.v13i09.7589.

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<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to he

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Zhang, Dongqing, and Yubing Han. "Time Series Prediction with RBF Neural Networks." Information Technology Journal 12, no. 14 (2013): 2815–19. http://dx.doi.org/10.3923/itj.2013.2815.2819.

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Alippi, C., V. Piuri, and F. Scotti. "Accuracy versus complexity in RBF neural networks." IEEE Instrumentation & Measurement Magazine 4, no. 1 (2001): 32–36. http://dx.doi.org/10.1109/5289.911171.

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27

Ososkov, G., and A. Stadnik. "Effective training algorithms for RBF-neural networks." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (2003): 529–31. http://dx.doi.org/10.1016/s0168-9002(03)00491-1.

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28

Wang, Ruliang, and Jie Li. "Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/852161.

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This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF) neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

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Bauer, EA. "Progressive trends on the application of artificial neural networks in animal sciences – A review." Veterinární Medicína 67, No. 5 (2022): 219–30. http://dx.doi.org/10.17221/45/2021-vetmed.

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In recent years, artificial neural networks have become the subject of intensive research in a number of scientific areas. The high performance and operational speed of neural models open up a wide spectrum of applications in various areas of life sciences. Objectives pursued by many scientists, who use neural modelling in their research, focus – among others – on intensifying real-time calculations. This study shows the possibility of using Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models of artificial neural networks for the future development of new methods for animal scie

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Sheng, Zhong Biao, and Xiao Rong Tong. "The Application of RBF Neural Networks in Curve Fitting." Advanced Materials Research 490-495 (March 2012): 688–92. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.688.

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Three means to realize function approach such as the interpolation approach, fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application. Based on basic principle of introduction, realization methods to non-linear are researched using interpolation function and fitting function in Matlab with example. It mainly studies the RBF neural networks and the training method. RBF neural network to proximate nonlinear function is designed and the desired effect is achieved through the training and simulation of ne

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Vladov, Serhii, Ruslan Yakovliev, Victoria Vysotska, Dmytro Uhryn, and Artem Karachevtsev. "Polymorphic Radial Basis Functions Neural Network." International Journal of Intelligent Systems and Applications 16, no. 4 (2024): 1–21. http://dx.doi.org/10.5815/ijisa.2024.04.01.

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The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based

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Fang, Yu, Zheng Wei Chang, Hao Wu, and Xian Feng Tang. "Identification the Faulty Components in Power Networks Based on Wide Area Information and RBF Neural Network." Applied Mechanics and Materials 568-570 (June 2014): 842–47. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.842.

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Using the wide area information of the IED, the identification faulty components network is constructed based on RBF neural network. Using the state information collected by line IED as the input vector, training samples matrix of identification faulty components network is established to train RBF neural network of faulty components identification, and to test the recognition network using the sample matrix under random failure, and then the faulty line IED can be identified, the faulty components can be determined. Experiments show that the new algorithm based on RBF has higher accuracy rate

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Li, Hui Jun, and Li Zhang. "Prediction of Tensile Strength Based on RBF Neural Network." Advanced Materials Research 476-478 (February 2012): 1309–12. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.1309.

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The objective of this research is to predict yarn tensile strength. The model of predicting yarn tensile strength is built based on RBF neural network. The RBF neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on RBF neural network are very precise and efficient.

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Xiao, Lijun, and Yan Luo. "The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching." Computational Intelligence and Neuroscience 2022 (June 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/4206857.

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The rapid growth of artificial intelligence technology has been deployed in art teaching and learning. Radial basis function (RBF) networks have a completely different design compared to most neural network architectures. Most neural networks consist of multiple layers that can introduce nonlinearity by repetitive application of nonlinear activation functions. In this research, people will study the application of the RBF neural network model based on deep learning in flower pattern design in art teaching. The image classification process is finding and labeling groups of pixels or vectors ins

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REYNOLDS, JAKE, and LIONEL TARASSENKO. "SPOKEN LETTER RECOGNITION WITH NEURAL NETWORKS." International Journal of Neural Systems 03, no. 03 (1992): 219–35. http://dx.doi.org/10.1142/s0129065792000188.

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Neural networks have recently been applied to real-world speech recognition problems with a great deal of success. This paper develops a strategy for optimising a neural network known as the Radial Basis Function classifier (RBF) on a large spoken letter recognition problem designed by British Telecom Research Laboratories. The strategy developed can be viewed as a compromise between a fully adaptive approach involving prohibitively large amounts of computation and a heuristic approach resulting in poor generalisation. A value for the optimal number of kernel functions is suggested and methods

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Liu, Xiao Qin, Hai Jun Sun, Shu Xian Deng, and Tai Dong Ji. "Fault Diagnosis System of Tennessee-Eastman Process Based on RBF Networks and Wavelet." Advanced Materials Research 546-547 (July 2012): 828–32. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.828.

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BP neural networks requires for the predicted number of hidden layer neurons and the corresponding predicted function. RBF distributed organizations can effectively address a large number of fault information. RBF algorithm is combined with the wavelet. The diagnosis system is simulated for multi-variable nonlinear Tennessee-Eastman Process (Tennessee - Eastman TE process).The results show that the fault diagnosis system based on wavelet RBF algorithm is better than the traditional BP neural networks and improved wavelet BP algorithm, and can effectively solve the problem of fault diagnosis.

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LACERDA, E., A. DE CARVALHO, and TERESA LUDERMIR. "EVOLUTIONARY OPTIMIZATION OF RBF NETWORKS." International Journal of Neural Systems 11, no. 03 (2001): 287–94. http://dx.doi.org/10.1142/s0129065701000734.

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One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This article discusses how Radial Basis Function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. A new strategy to optimize RBF networks using genetic algorithms is proposed, which includes new representation, crossover operator and the use of a multiobjective optimization criterion. Exp

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Fu, Yanfang, Dengdeng Guo, Qiang Li, Liangxin Liu, Shaochun Qu, and Wei Xiang. "Digital Twin Based Network Latency Prediction in Vehicular Networks." Electronics 11, no. 14 (2022): 2217. http://dx.doi.org/10.3390/electronics11142217.

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Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, we

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ALEXANDRIDIS, ALEX. "EVOLVING RBF NEURAL NETWORKS FOR ADAPTIVE SOFT-SENSOR DESIGN." International Journal of Neural Systems 23, no. 06 (2013): 1350029. http://dx.doi.org/10.1142/s0129065713500299.

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This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input–output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forge

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Bombiński, Sebastian, and Joanna Kossakowska. "Algorithm of the tool condition monitoring system based on many neural networks." Mechanik 90, no. 3 (2017): 220–23. http://dx.doi.org/10.17814/mechanik.2017.3.42.

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Presented is a comparison of different methods of estimating tool wear – obtained for group of RBF neural networks, hierarchical methods and the standard time counting. The analysis of the signals from the machining process carried out for three different experiments, clearly demonstrating the effect of presented methods. The results obtained for group of RBF neural networks are similar to results obtained for hierarchical methods.

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Dawson, C. W., C. Harpham, R. L. Wilby, and Y. Chen. "Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China." Hydrology and Earth System Sciences 6, no. 4 (2002): 619–26. http://dx.doi.org/10.5194/hess-6-619-2002.

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Abstract. While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In additi

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Zhang, Li, Min Zheng, Dajun Du, et al. "State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks." Complexity 2020 (November 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/8840240.

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Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temper

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Novizon and Zulkurnain Abdul-Malek. "Neutral Networks for Fault Classification: Comparison between Feed-Forward Back-Propagation, RBF and LVQ Neural Network." Applied Mechanics and Materials 818 (January 2016): 96–100. http://dx.doi.org/10.4028/www.scientific.net/amm.818.96.

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— Neural networks are frequently used as a classifier for tasks in many classifications. However there are disadvantages in terms of amount of training data required, and length of training time. This paper, develop an intelligent diagnosis system for zinc oxide (ZnO) surge arrester fault classification. First the features were extracted from 600 ZnO surge arrester thermal images and leakage currents. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the ZnO surge arrester fault condition effectively. Three

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Tabari, Hossein, and P. Hosseinzadeh Talaee. "Reconstruction of river water quality missing data using artificial neural networks." Water Quality Research Journal 50, no. 4 (2015): 326–35. http://dx.doi.org/10.2166/wqrjc.2015.044.

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The monitoring of river water quality is important for human life and the health of the environment. However, water quality studies in many parts of the world, especially in developing countries, are restricted by the existence of missing data. In this study, the efficiency of the multilayer perceptron (MLP) and radial basis function (RBF) networks for recovering the missing values of 13 water quality parameters was examined based on data from five stations located along the Maroon River, Iran. The monthly values of other existing water quality parameters were used as input variables to the ML

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Liu, Yunbing. "Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management." Journal of Control Science and Engineering 2022 (June 30, 2022): 1–6. http://dx.doi.org/10.1155/2022/7025223.

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Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the BP network and RBF network commonly used in neural networks, the performance of the two networks is compared and demonstrated by MATLAB program, which proves that RBF neural network can significantly improve the a

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Nedbalek, Jakub. "Rbf Neural Networks for Function Approximation in Dynamic Modelling." Journal of Konbin 8, no. 1 (2008): 223–32. http://dx.doi.org/10.2478/v10040-008-0115-6.

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Rbf Neural Networks for Function Approximation in Dynamic ModellingThe paper demonstrates the comparison of Monte Carlo simulation algorithm with neural network enhancement in the reliability case study. With regard to process dynamics, we attempt to evaluate the tank system unreliability related to the initiative input parameters setting. The neural network is used in equation coefficients calculation, which is executed in each transient state. Due to the neural networks, for some of the initial component settings we can achieve the results of computation faster than in classical way of coeff

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Khazaee, Ali. "Automated Cardiac Beat Classification Using RBF Neural Networks." International Journal of Modern Education and Computer Science 5, no. 3 (2013): 42–48. http://dx.doi.org/10.5815/ijmecs.2013.03.06.

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Ham, Fredric M., Kamel Rekab, Ranjan Acharyya, and Young Chan Lee. "Infrasound signal classification using parallel RBF Neural Networks." International Journal of Signal and Imaging Systems Engineering 1, no. 3/4 (2008): 155. http://dx.doi.org/10.1504/ijsise.2008.026787.

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Buzzi, C., L. Grippo, and M. Sciandrone. "Convergent Decomposition Techniques for Training RBF Neural Networks." Neural Computation 13, no. 8 (2001): 1891–920. http://dx.doi.org/10.1162/08997660152469396.

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In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers. Then we define a decomposition algorithm in which the selection of the center locations is split into sequential minimizations with respect to each center, and we give a suitable criterion for choosing the centers that must be updated at each step. We prove the global convergence of the propose

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CUI, XiangQun, and Na HU. "Model updating of LAMOST using RBF neural networks." SCIENTIA SINICA Physica, Mechanica & Astronomica 43, no. 5 (2013): 678–86. http://dx.doi.org/10.1360/132012-1019.

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