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Journal articles on the topic 'Neural network RBF'

Author: Grafiati

Published: 4 May 2024

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1

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, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.

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2

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 hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.

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3

Yakovyna, V. S. "Software failures prediction using RBF neural network." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 2 (June 15, 2015): 111–18. http://dx.doi.org/10.15276/opu.2.46.2015.20.

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4

Wen, Hui, Tao Yan, Zhiqiang Liu, and Deli Chen. "Integrated neural network model with pre-RBF kernels." Science Progress 104, no. 3 (July 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 artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.

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5

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 accuracy of deformation prediction. By comparing the results, the maximum relative error of BP network prediction is 18.59%, while the maximum relative error of RBF network prediction is 29.16%, and the average relative error of 13P network prediction is 7.02%, while the average relative error of RBF network prediction value is 10.5%. The comprehensive error of network prediction is 6.1%, RBF network prediction is 8.52%, the standard deviation RMSE of BP network prediction error is 15.347, and that of RBF network prediction error is 21.401, and it shows that the prediction accuracy of BP network is higher than that of RBF network.

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6

Yu, Fa Hong, Mei Jia Chen, and Wei Zhi Liao. "A Novel Learning Evaluation Method Based on RBF Neural Network." Applied Mechanics and Materials 385-386 (August 2013): 1697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1697.

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There are many learning evaluation methods, but most of them are subjective, which contains a lot of man-made factors. This paper presents a new learning evaluation method based on radial basis function (RBF) neutral network. By analysis the orthogonal least squares for RBF and determines the center of the basis functions, the model of RBF neural network was constructed. Experimental studies show that the Method Based on RBF Neural Network is effective for learning Evaluation.

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7

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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8

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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9

Tsoulos, Ioannis G., Alexandros Tzallas, and Evangelos Karvounis. "A Two-Phase Evolutionary Method to Train RBF Networks." Applied Sciences 12, no. 5 (February 25, 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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10

Yu, Ying. "GDP Economic Forecasting Model Based on Improved RBF Neural Network." Mathematical Problems in Engineering 2022 (September 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/7630268.

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Among the existing GDP forecasting methods, time series forecasting and regression model forecasting are the two most commonly used forecasting methods. However, traditional macroeconomic forecasting models are unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic macroeconomic systems due to the influence of multiple confounding factors. In order to solve the above problems, a GDP economic forecasting model based on an improved RBF neural network is proposed. First, the main traditional GDP forecasting methods are analyzed. Then, RBF neural networks are used to solve the problem that traditional forecasting technology methods cannot handle multi-factor complex nonlinearities well. Second, to further improve the convergence speed and accuracy of the RBF neural network learning algorithm, the Shuffled Frog Leaping Algorithm with global search capability and high practicality is fused into the RBF network training. Finally, the improved RBF neural network is used to build a GDP economic forecasting model. The performance of the Shuffled Frog Leaping Algorithm and the improved RBF neural network was tested using the approximation of Hermit polynomials and the Iris classification problem as simulation examples. The experimental results show that the improved RBF neural network-based GDP economic forecasting model achieves more accurate forecasting accuracy than other forecasting methods.

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11

Schmitt, Michael. "Descartes' Rule of Signs for Radial Basis Function Neural Networks." Neural Computation 14, no. 12 (December 1, 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 parameters for the complexity of computing with RBF neural networks.

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12

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 (March 13, 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 fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

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13

Ma, Lili, Jiangping Liu, and Jidong Luo. "Method of Wireless Sensor Network Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 09 (September 22, 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 heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm. Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.</span></span></p>

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14

Liu, Li Long, Jun Yu Li, Chen Hui Cai, and Guo Biao Lin. "Research on the GPS Elevation Fitting with RBF Neural Network Model Considering Effects of Sample Data Preprocessing." Applied Mechanics and Materials 568-570 (June 2014): 817–21. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.817.

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RBF neural network and three kinds of preprocessing methods are introduced, and this paper used these preprocessing methods combined with RBF neural network and strict RBF neural network to perform elevation fitting. Comparing and analyzing the fitting results, the results show that preprocessing methods can affect elevation fitting results. Centralized preprocessing data maximum improves RBF neural network elevation fitting precision, and it also let RBF neural network have stronger generalization ability. Normalization preprocessing methods are not necessarily optimal. It is essential for us to choose preprocessing method to fit the elevation.

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15

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 network. As is shown from the simulation results, RBF network has strong nonlinear processing and approximating features, and RBF network model has the characteristics of high precision, fast learning speed for the prediction.

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16

Gan, Xu Sheng, and Hai Long Gao. "Research on Learning Algorithm of RBF Neural Network Based on Extended Kalman Filter." Advanced Materials Research 989-994 (July 2014): 2705–8. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2705.

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To improve the learning capability of Radial Basis Function (RBF) neural network, a RBF neural network algorithm based on Extended Kalman Filter (EKF) is proposed. First the basic idea of EKF algorithm and RBF neural network are introduced, and then EKF is used to optimize the parameters combination of RBF neural network to obtain the better model. The experiment proves its feasibility.

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17

Bahita, Mohamed, and Khaled Belarbi. "Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator." Serbian Journal of Electrical Engineering 8, no. 3 (2011): 307–23. http://dx.doi.org/10.2298/sjee1103307b.

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In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

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18

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 neural network was then applied to diagnose stream turbine vibration faults. The diagnosis results show that the proposed method possesses higher convergence speed and diagnosis precision, and is a very effective turbine fault diagnosis method.

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19

Chen, Tongqing, Lei Wang, Xijuan Jiang, Yubin Wang, and Kai Yan. "Finite Element Model Modification of Arch Bridge Based on Radial Basis Function Neural Network." E3S Web of Conferences 136 (2019): 04033. http://dx.doi.org/10.1051/e3sconf/201913604033.

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Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function. In this paper, the finite element model of a through tied arch bridge is modified based on Neural Network. The approximation function of RBF neural network is utilized to fit the implicit function relationship between the response of the bridge and its design parameters. Then the finite element model of the bridge structure is modified. The results show that RBF neural network is efficient to modify the model of a through tied arch bridge.

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20

Zeng, Qing Wei, Zhi Hai Xu, and Geng Sheng Deng. "Study on Dynamic Load Balance Method Based on Genetic Algorithm and RBF Neural Network." Advanced Materials Research 108-111 (May 2010): 207–10. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.207.

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Dynamic load balance is the critical pivotal role of network parallel and distributed computing. In order to solve the drawbacks of BP neural network, RBF neural network(RBFNN) is applied to dynamic load balance of network. And genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network, the method is well suited for searching global optimal values. In the paper, genetic algorithm and RBF neural network (GA-RBFNN) is adopted to dynamic load balance of network. The cases are applied to study the ability of dynamic load balance. The experimental results indicate that GA- RBF neural network is better dynamic load balance method than BP neural network.

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21

Tang, Xiaowei, Bing Xu, and Zichen Xu. "Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network." Applied Sciences 13, no. 5 (March 2, 2023): 3230. http://dx.doi.org/10.3390/app13053230.

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A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network’s initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model’s validity and feasibility were established. The study findings may provide some reference values for the reactor’s temperature prediction.

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22

张, 轶. "Option Pricing with BP Neural Network and RBF Neural Network." Statistical and Application 02, no. 04 (2013): 119–26. http://dx.doi.org/10.12677/sa.2013.24018.

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23

Leow, Shoun Ying, Keem Siah Yap, and Shen Yuong Wong. "Harmonic current classification using hybrid FAM-RBF neural network." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1551. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1551-1558.

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In this paper, the type of customers of electricity in Malaysia is classified into the type of electricity consumers based on the harmonic current data. A hybrid of Fuzzy Adaptive Resonance Theory with Mapping Algorithm (Fuzzy ARTMAP) and Radial Basis Function (RBF) neural network is developed (namely FAM-RBF), and it is used to classify the harmonic current into types of consumers. The result of the proposed neural network is discussed, and compared with other neural networks in this paper. The comparison result shows that the proposed FAM-RBF obtained the best performance result and is a truthful neural network to be used in this application.

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Lv, Bailin, and Yizhang Jiang. "Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network." Computational Intelligence and Neuroscience 2021 (November 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/8495288.

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Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.

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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 chattering and parameters estimation.

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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 inside an image based on rules. Deep learning is a type of machine learning that uses artificial neural networks to replicate the structure and function of the human brain. The proposed model uses the RBF neural network-based deep learning model in flower pattern design in art teaching and provides efficient results.

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27

Lee, N. K., and D. Wang. "Realization of Generalized RBF Network." Journal of IT in Asia 1, no. 1 (July 21, 2017): 1–16. http://dx.doi.org/10.33736/jita.400.2005.

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This paper aims at developing techniqus for design and implementation of neural classifiers. Based on our previous study on generalized RBF neural network architecture and learning criterion function for parameter optimization, this work addresses two realization issues, i.e. supervised input features selection and genetic computation techniques for tuning classifiers. A comparative study on classifiation performance is carried on by a set of protein sequence data.

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Wu, Hong Qi, and Xiao Bin Li. "Research on Intelligent Diagnosis Technology of Transformer Fault." Applied Mechanics and Materials 385-386 (August 2013): 589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.589.

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In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

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29

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 (August 31, 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 addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

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30

Xie, Xiao Zhu, and Xing Lai Guan. "A Novel Control Scheme Based on Improved RBF Neural Network." Applied Mechanics and Materials 182-183 (June 2012): 1313–17. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1313.

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A novel control scheme based on an improved RBF neural network and PID control method is proposed. When used the RBF network in the PID controller, if RBF network provides the parameter to revise PID while it does not have training finished, the controller will oscillated or even to diverge. As the structure is more complex, and the adjustment speed is slower, the improvements are made to the standard RBF neural network trained algorithm. Uses the matrix operation substitute the iterative algorithm, may avoid the above question effectively. Finally, choosing a certain type of PID controller, the improved RBF neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved RBF neural network algorithm can avoid oscillated and diverge.

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31

Hu, Peitao, and Liu Changliang. "Soft-sensing of NOx content in power Station based on BP Neural Network, RBF Neural Network and PCA-RBF Neural Network." IOP Conference Series: Materials Science and Engineering 392, no. 6 (August 3, 2018): 062180. http://dx.doi.org/10.1088/1757-899x/392/6/062180.

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32

Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "Fault Diagnosis of Induction Motors Based on RBF Neural Network." Applied Mechanics and Materials 462-463 (November 2013): 85–88. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.85.

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In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

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Yu, Cheng Bo, Jun Tan, Lei Yu, and Yin Li Tian. "A Finger Vein Recognition Method Based on PCA-RBF Neural Network." Applied Mechanics and Materials 325-326 (June 2013): 1653–58. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1653.

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This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

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Liu, Wei, Feifan Wang, Xiawei Yang, and Wenya Li. "Upset Prediction in Friction Welding Using Radial Basis Function Neural Network." Advances in Materials Science and Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/196382.

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This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

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Liu, Xi Mei, Xiao Hui Yao, Qian Zhao, and Hong Mi Guo. "Application of RBF Neural Network in Fault Diagnosis for Transmission Gear." Advanced Materials Research 433-440 (January 2012): 7563–68. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7563.

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A method for transmission gearbox fault diagnosis is put forward in this paper by using radial basis function neural network (RBF network). A RBF neural network is created to simulate the gearbox fault diagnosis using Matlab neural network toolbox. Compared with BP neural network, RBF network is superior to the former in accuracy and speed according to the simulate results. This method is accurate and credible in gear fault diagnosis, and it has a broad application prospect in mechanical fault diagnosis.

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Yu, Meixia, Xiaoping Zheng, and Chuanhui Zhao. "Research on the Prediction Method of Clock Tester Calibration Data Based on Radial Basis Function Neural Network." Electronics 12, no. 22 (November 17, 2023): 4677. http://dx.doi.org/10.3390/electronics12224677.

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A radial basis function (RBF) neural network-based calibration data prediction model for clock testers is proposed to address the issues of fixed calibration cycles, low efficiency, and waste of electrical energy. This provides a new method for clock tester traceability calibration. First, analyze the mechanism of clock tester calibration parameters and the influencing factors of prediction targets. Based on the learning rules of an RBF neural network, determine the data types of training and testing sets. Second, normalize the training and testing data to avoid the adverse effects of data characteristics and distribution differences on the prediction model. Finally, based on different prediction objectives, time-driven and data-driven calibration data prediction models are constructed using RBF neural networks. Through simulation analysis, it is shown that an RBF neural network is superior to a BP neural network in predicting clock tester calibration data, and time-driven prediction accuracy is superior to data-driven prediction accuracy. Moreover, the prediction error and mean square error of both prediction models are on the order of 10−9, meeting the prediction accuracy requirements.

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Huang, Yifan, Ziwei Xiong, Yubin Zhao, Wei Wang, and Xingming Xu. "Mid-long-term prediction of electrical load based on particle swarm optimization and RBF neural network." Journal of Physics: Conference Series 2355, no. 1 (October 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2355/1/012049.

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Abstract Accurate mid-long-term load forecasting is of great significance to power system planning and safe operation. Therefore, this paper proposed a mid-long-term prediction of electrical load based on PSO and RBF neural networks. Based on RBF neural network, the mid-long-term prediction model of electrical load is established, and the prediction parameters of the RBF neural network are iterated by PSO to obtain the optimal parameter value, which is the key to improving the prediction accuracy. The comparison of calculation results shows that it can improve the accuracy of mid-long-term electric load forecasting.

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Yin, Rongwang, Qingyu Li, Peichao Li, and Detang Lu. "Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network." Scientific Programming 2020 (July 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/6810903.

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In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.

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Li, Yong Wei, Zhi Gang Ye, and Chao Chao Huo. "The Method Research of Grey Neural Network Control Based on Data." Applied Mechanics and Materials 602-605 (August 2014): 1131–34. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1131.

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A control process based on data often contains large amounts of sample data. The accuracy of system is always influenced by the randomness of the initial data when the RBF neural network is used to predict model. However, the grey accumulated generating operation (AGO) can reduce the effect of randomness of the initial data, which is able to make data more regular. Based on the two points above, a new kind of method is proposed, which is called grey RBF neural network. This method not only can reduce the randomness, speed up the network convergence, but also improve the modeling accuracy. The grey RBF neural network can be proved to be feasible and effective by applying the grey RBF neural network to the synthetic ammonia decarburization process, and comparing the simulation results with the results which was only using RBF network.

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Soper, Daniel S. "Using an Opportunity Matrix to Select Centers for RBF Neural Networks." Algorithms 16, no. 10 (September 23, 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. Traditionally, the locations for the RBF nodes’ centers are chosen either through random sampling of the training data or by using k-means clustering. The current paper proposes a new algorithm for selecting the locations of the centers by relying on a structure known as an “opportunity matrix”. The performance of the proposed algorithm is compared against that of the random sampling and k-means clustering methods using a large set of experiments involving both a real-world dataset from the steel industry and a variety of mathematical and statistical functions. The results indicate that the proposed opportunity matrix algorithm is almost always much better at selecting locations for an RBF network’s centers than either of the two traditional techniques, yielding RBF neural networks with superior function approximation capabilities.

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Tan, Yun Liang, and Ze Zhang. "A RBF Neural Network Approach for Fitting Creep Curve of Sandstone." Advanced Materials Research 171-172 (December 2010): 274–77. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.274.

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In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

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Zhang, Meifeng, Yongxin Li, Jianwen Cai, Fuhao Chen, and Xin Miao. "Research on fault diagnosis of diesel engine based on PCA-RBF neural network." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840099. http://dx.doi.org/10.1142/s0217984918400997.

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In order to improve the fault diagnosis rate and efficiency of diesel engine, the PCA-RBF neural network as a new algorithm was constructed by combing the character extraction ability of PCA with the nonlinear approximation ability of RBF neural network. Firstly, eight factors which affected the fault types of diesel engine were analyzed and three principal components were extracted by PCA. Secondly, the data obtained from the three principal components were taken as the input of RBF neural network which was trained and tested. Finally, the PCA-RBF neural network was verified through simulation. The simulation results show that the network has fewer training steps, less training and higher training accuracy.

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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 and better fault-tolerant.

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Yang, Yongkang, Qiaoyi Du, Chenlong Wang, and Yu Bai. "Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model." Energies 13, no. 22 (November 21, 2020): 6112. http://dx.doi.org/10.3390/en13226112.

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Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

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Yang, Qin, Zhaofa Ye, Xuzheng Li, Daozhu Wei, Shunhua Chen, and Zhirui Li. "Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network." Sensors 21, no. 11 (May 24, 2021): 3651. http://dx.doi.org/10.3390/s21113651.

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Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.

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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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Chen, Dong. "Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO)." Computational Intelligence and Neuroscience 2021 (July 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/6819493.

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This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.

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Chang, Wen Yeau. "State of Charge Estimation for LFP Battery Using the Hybrid Method." Applied Mechanics and Materials 431 (October 2013): 221–25. http://dx.doi.org/10.4028/www.scientific.net/amm.431.221.

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A method to accurately estimate the state of charge (SOC) for LiFePO4(LFP) batteries is urgently required, to address the issues associated with the increased use of LPF batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network and enhanced particle swarm optimization (EPSO) algorithm for SOC estimating. With a RBF neural network structure, the EPSO algorithm is used to tune the parameters of the RBF neural network, including the centers and widths of the RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using 12.6V, 52Ah LFP batteries under varied discharging condition. The effectiveness of the proposed method is compared with the Coulomb integration method and the back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.

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Nedbalek, Jakub. "Rbf Neural Networks for Function Approximation in Dynamic Modelling." Journal of Konbin 8, no. 1 (January 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 coefficients calculating and substituting into the equation.

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Wilkins, M. F., Lynne Boddy, C. W. Morris, and R. R. Jonker. "Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks." Applied and Environmental Microbiology 65, no. 10 (October 1, 1999): 4404–10. http://dx.doi.org/10.1128/aem.65.10.4404-4410.1999.

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ABSTRACT We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91.5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from “novel” species (species not present in the training data) were analyzed.

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