Journal articles on the topic 'Radial basis function (RBF) model'
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Hu, Shan-Feng, Hong-Bin Zhu, and Lei Zhao. "Radial basis function and its application in tourism management." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840054. http://dx.doi.org/10.1142/s0217984918400547.
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In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.
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Wu, Yue, Hui Wang, Biaobiao Zhang, and K. L. Du. "Using Radial Basis Function Networks for Function Approximation and Classification." ISRN Applied Mathematics 2012 (March 6, 2012): 1–34. http://dx.doi.org/10.5402/2012/324194.
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The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.
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Holmes, C. C., and B. K. Mallick. "Bayesian Radial Basis Functions of Variable Dimension." Neural Computation 10, no. 5 (July 1, 1998): 1217–33. http://dx.doi.org/10.1162/089976698300017421.
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A Bayesian framework for the analysis of radial basis functions (RBF) is proposed that accommodates uncertainty in the dimension of the model. A distribution is defined over the space of all RBF models of a given basis function, and posterior densities are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select the architecture during the modeling process. The resulting networks are shown to adjust their size to the complexity of the data.
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Shao, Changpeng. "Quantum speedup of training radial basis function networks." Quantum Information and Computation 19, no. 7&8 (June 2019): 609–25. http://dx.doi.org/10.26421/qic19.7-8-6.
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Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system, which is time consuming classically. Based on HHL algorithm, we propose two quantum algorithms to train RBF networks. To apply the HHL algorithm, we choose using the Hamiltonian simulation algorithm proposed in [P. Rebentrost, A. Steffens, I. Marvian and S. Lloyd, Phys. Rev. A 97, 012327, 2018]. However, to use this result, an oracle to query the entries of the matrix of the network should be constructed. We apply the amplitude estimation technique to build this oracle. The final results indicate that if the centers of the RBF network are the training samples, then the quantum computer achieves exponential speedup at the number and the dimension of training samples over the classical computer; if the centers are determined by the K-means algorithm, then the quantum computer achieves quadratic speedup at the number of samples and exponential speedup at the dimension of samples.
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El Shafie, Amr H., A. El-Shafie, A. Almukhtar, Mohd R. Taha, Hasan G. El Mazoghi, and A. Shehata. "Radial basis function neural networks for reliably forecasting rainfall." Journal of Water and Climate Change 3, no. 2 (June 1, 2012): 125–38. http://dx.doi.org/10.2166/wcc.2012.017.
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Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.
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Lin, W., M. H. Wu, and S. Duan. "Engine Test Data Modelling by Evolutionary Radial Basis Function Networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 6 (June 1, 2003): 489–97. http://dx.doi.org/10.1243/095440703766518113.
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The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.
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Park, Byungkyu, Carroll J. Messer, and Thomas Urbanik. "Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network." Transportation Research Record: Journal of the Transportation Research Board 1651, no. 1 (January 1998): 39–47. http://dx.doi.org/10.3141/1651-06.
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A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.
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Yang, Qing Wei, Nai Chao Wang, and Ma Lin. "Application of Radial Basis Function Neural Network to Support Concept Evaluation." Advanced Materials Research 472-475 (February 2012): 1926–31. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.1926.
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In order to solve the problem that how to evaluate the complex system support concept, an evaluation method based on Radial Basis Function (RBF) neural network model was presented. Through researching the support system overall design characteristics and elements of support, on this basis, evaluation parameters of support concept were abstracted. Support concept evaluation model based on RBF was established and a mature and stable RBF neural network was trained to calculate the comprehensive evaluation value for support concept. Finally, the further demonstration and verification of the method are given through specific case application and compared with the result for evaluation results of data envelopment analysis (DEA) model.
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Ku, Cheng-Yu, Chih-Yu Liu, and Frank T. C. Tsai. "A Novel Radial Basis Function Approach for Infiltration-Induced Landslides in Unsaturated Soils." Water 14, no. 7 (March 25, 2022): 1036. http://dx.doi.org/10.3390/w14071036.
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In this article, the modeling of infiltration--induced landslides, in unsaturated soils using the radial basis function (RBF) method, is presented. A novel approach based on the RBF method is proposed to deal with the nonlinear hydrological process in the unsaturated zone. The RBF is first adopted for curve fitting to build the representation of the soil water characteristic curve (SWCC) that corresponds to the best estimate of the relationship between volumetric water content and matric suction. The meshless method with the RBF is then applied to solve the nonlinear Richards equation with the infiltration boundary conditions. Additionally, the fictitious time integration method is adopted in the meshless method with the RBF for tackling the nonlinearity. To model the stability of the landslide, the stability analysis of infinite slope coupled with the nonlinear Richards equation considering the fluctuation of transient pore water pressure is developed. The validation of the proposed approach is accomplished by comparing with exact solutions. The comparative analysis of the factor of safety using the Gardner model, the van Genuchten model and the proposed RBF model is provided. Results illustrate that the RBF is advantageous for reconstructing the SWCC with better estimation of the relationship than conventional parametric Gardner and van Genuchten models. We also found that the computed safety factors significantly depend on the representation of the SWCC. Finally, the stability of landslides is highly affected by matric potential in unsaturated soils during the infiltration process.
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Chen, Gan. "Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm." Computational Intelligence and Neuroscience 2022 (April 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/6314262.
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Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.
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Nik, Amirhossein, and Jawad Faiz. "Optimization of synchronous reluctance motor based on radial basis network." Serbian Journal of Electrical Engineering 17, no. 2 (2020): 223–34. http://dx.doi.org/10.2298/sjee2002223n.
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This paper presents surrogate-model based optimization for synchronous reluctance motor (SynRm) with transversally laminated rotor. A radial basis function (RBF) model with 12 input variables and three outputs is first trained. A dataset is obtained using finite element method to estimate parameters of RBF model. By building RBF model, the RBF network can predicts the outputs of the SynRm with good accuracy Using non-dominated sorting genetic algorithm (NSGA II), pareto front is obtained. The SynRm is designed to maximize the maximum developed torque and power factor of the motor with constrained torque ripple.
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Du, H., L. Zhang, and X. Shi. "Reconstructing cylinder pressure from vibration signals based on radial basis function networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 215, no. 6 (June 1, 2001): 761–67. http://dx.doi.org/10.1243/0954407011528338.
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This paper presents an approach to reconstruct internal combustion engine cylinder pressure from the engine cylinder head vibration signals, using radial basis function (RBF) networks. The relationship between the cylinder pressure and the engine cylinder head vibration signals is analysed first. Then, an RBF network is applied to establish the non-parametric mapping model between the cylinder pressure time series and the engine cylinder head vibration signal frequency series. The structure of the RBF network model is presented. The fuzzy c-means clustering method and the gradient descent algorithm are used for selecting the centres and training the output layer weights of the RBF network respectively. Finally, the validation of this approach to cylinder pressure reconstruction from vibration signals is demonstrated on a two-cylinder, four-stroke direct injection diesel engine, with data from a wide range of speed and load settings. The prediction capabilities of the trained RBF network model are validated against measured data.
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NUNES DE CASTRO, LEANDRO, and FERNANDO J. VON ZUBEN. "AUTOMATIC DETERMINATION OF RADIAL BASIS FUNCTIONS: AN IMMUNITY-BASED APPROACH." International Journal of Neural Systems 11, no. 06 (December 2001): 523–35. http://dx.doi.org/10.1142/s0129065701000941.
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The appropriate operation of a radial basis function (RBF) neural network depends mainly upon an adequate choice of the parameters of its basis functions. The simplest approach to train an RBF network is to assume fixed radial basis functions defining the activation of the hidden units. Once the RBF parameters are fixed, the optimal set of output weights can be determined straightforwardly by using a linear least squares algorithm, which generally means reduction in the learning time as compared to the determination of all RBF network parameters using supervised learning. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBFs. The approach proposed here is inspired by models derived from the vertebrate immune system, that will be shown to perform unsupervised cluster analysis. The algorithm is introduced and its performance is compared to that of the random, k-means center selection procedures and other results from the literature. By automatically defining the number of RBF centers, their positions and dispersions, the proposed method leads to parsimonious solutions. Simulation results are reported concerning regression and classification problems.
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LONG, NANYE, DANIEL GIANOLA, GUILHERME J. M. ROSA, KENT A. WEIGEL, ANDREAS KRANIS, and OSCAR GONZÁLEZ-RECIO. "Radial basis function regression methods for predicting quantitative traits using SNP markers." Genetics Research 92, no. 3 (June 2010): 209–25. http://dx.doi.org/10.1017/s0016672310000157.
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SummaryA challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype–phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype–phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0·1, 0·25, 0·5, 0·75 and 0·9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive+dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype–phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.
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Troy, Troy, and Pranowo Pranowo. "Transformasi Ruang 2D Ke 3D Pada Animasi Wajah Berbasis Data Marker Menggunakan Radial Basis Function." Journal of Animation & Games Studies 2, no. 2 (December 18, 2016): 229. http://dx.doi.org/10.24821/jags.v2i2.1422.
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Computer facial animation aims to create an animated character expression as natural as possible as well as human facial expressions. Using the data marker catches facial motion capture, will be determined the location of the feature points of 3D face models to follow the motion of the marker points of human faces.To overcome the morphological differences between the face of the source with the character's face, then applied with radial basis retargeting process mapping so that the character's face can still display the natural expression. Using the data marker 2D, Radial Basis Function (RBF) space transformation was applied to determine the position of the feature points on the 3D face models.RBF space transformation has good ability in determining the appropriate facial motion marker points on a human face to the character's face. Motion that occurs in 3D face models is scaled according to the relative scale between the source and the target. Keywords: facial animation, radial basis function, marker data. AbstrakTeknik komputasi yang dikembangkan pada animasi wajah bertujuan untuk menciptakan ekspresi pada wajah karakter animasi senatural mungkin seperti layaknya ekspresi pada wajah manusia. Menggunakan data marker pada citra 2D wajah manusia, komputer menangkap pergerakan marker tersebut kemudian menetukan lokasi titik fitur yang pada wajah model 3D (karakter animasi).Untuk mengatasi perbedaan morfologi pada wajah manusia yang menjadi sumber acuan ekspresi dengan wajah model 3D yang menjadi target animasi, maka diterapkan transformasi ruang Radial Basis Function (RBF). RBF digunakan untuk menentukan posisi titik fitur pada wajah model 3D berdasarkan posisi titik marker pada citra 2D wajah manusia.Transformasi ruang RBF memiliki kemampuan yang baik dalam pemetaan ulang titik marker dari wajah manusia ke titik fitur pada wajah karakter animasi. Pergerakan yang terjadi pada wajah model 3D berdasar pada skala relatif antara titik marker pada citra 2D wajah manusia dengan titik fitur pada wajah model 3D.Kata kunci: animasi wajah, radial basis function, data marker.
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Zhang, Bi, Jia Yang Wang, and Su Lan Zhang. "A New PSO-RBF Model for Groundwater Quality Assessment." Advanced Materials Research 463-464 (February 2012): 922–25. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.922.
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There are three adjustable parameters in the radial basis function, the center of the basis function cj, the width parameter and the output unit weight wj. Through optimization the parameters of the radial basis function by Particle swarm optimization algorithm, a neural network model of underground water is generated, which is used to study the grade of underground water in the ten monitoring points of the black dragon hole. By applying the PSO-RBF model to underground water assessment in the ten monitoring points of the black dragon hole, the results of this evaluation, which correspond with the real conditions, are basically in accord with those obtained by other evaluation methods, and also show the practicability to groundwater quality assessment.
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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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Zhang, Hong, Zhi Guo Lei, Jian Guo, and Zhao Yu Pian. "Short Term Load Forecasting Based on Improved RBF Neural Network." Advanced Materials Research 860-863 (December 2013): 2610–13. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2610.
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An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.
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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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Liu, Rui Fang. "Wind Power Generation Prediction by Particle Swarm Optimization Algorithm and RBF Neural Network." Advanced Materials Research 433-440 (January 2012): 2099–102. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2099.
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Wind power generation trend prediction is the important to make the plan on the development of wind power generation. Wind power generation prediction by particle swarm optimization algorithm and RBF neural network in the paper. As the connection weights between the hidden layer and output layer, the centers of radial basis function in hidden layer and the widths of radial basis function in hidden layer have a great influence on the prediction results of RBF neural network,particle swarm optimization which has a great global optimization ability is used to optimize the three parameters including the connection weights between the hidden layer and output layer, the centers of radial basis function in hidden layer and the widths of radial basis function in hidden layer. It is indicated that the hybrid model of particle swarm optimization algorithm and RBF neural network has better prediction ability than BP neural network.
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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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Tomczyk, Krzysztof, Marcin Piekarczyk, and Grzegorz Sokal. "Radial Basis Functions Intended to Determine the Upper Bound of Absolute Dynamic Error at the Output of Voltage-Mode Accelerometers." Sensors 19, no. 19 (September 25, 2019): 4154. http://dx.doi.org/10.3390/s19194154.
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In this paper, we propose using the radial basis functions (RBF) to determine the upper bound of absolute dynamic error (UAE) at the output of a voltage-mode accelerometer. Such functions can be obtained as a result of approximating the error values determined for the assumed-in-advance parameter variability associated with the mathematical model of an accelerometer. This approximation was carried out using the radial basis function neural network (RBF-NN) procedure for a given number of the radial neurons. The Monte Carlo (MC) method was also applied to determine the related error when considering the uncertainties associated with the parameters of an accelerometer mathematical model. The upper bound of absolute dynamic error can be a quality ratio for comparing the errors produced by different types of voltage-mode accelerometers that have the same operational frequency bandwidth. Determination of the RBFs was performed by applying the Python-related scientific packages, while the calculations related both to the UAE and the MC method were carried out using the MathCad program. Application of the RBFs represent a new approach for determining the UAE. These functions allow for the easy and quick determination of the value of such errors.
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Zhang, Huan, Menghong Yu, and Wei Yuan. "Cutting Process Model Design of Cutter Suction Dredger Based on Auto Regressive eXogenous and Radial Basis Function model." Journal of Physics: Conference Series 2137, no. 1 (December 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/2137/1/012064.
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Abstract The dredging operation of the strander dredger is complex, and the mathematical model established according to its key equipment characteristics is not possible to describe such a system having time degeneration and non-linear. Therefore, based on the analysis of mud formation process of dredger, RBF-ARX model is used to model the cutting process, and mud concentration is taken as the output. This modeling method is a combination model based on the theory of Auto-Regressive eXogenous (ARX) model and Gauss radial basis function (Radial Basis Function) neural network (RBF). The comparison between the simulation results and the actual data shows that the model can accurately describe the dynamic characteristics of cutter suction dredger in the cutting process.
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HUANG, JEFFREY, and HARRY WECHSLER. "EYE DETECTION USING OPTIMAL WAVELET PACKETS AND RADIAL BASIS FUNCTIONS (RBFs)." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1009–25. http://dx.doi.org/10.1142/s0218001499000562.
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The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to the anchoring on model-based schemes. This paper introduces a novel approach for the eye detection task using optimal wavelet packets for eye representation and Radial Basis Functions (RBFs) for subsequent classification ("labeling") of facial areas as eye versus non-eye regions. Entropy minimization is the driving force behind the derivation of optimal wavelet packets. It decreases the degree of data dispersion and it thus facilitates clustering ("prototyping") and capturing the most significant characteristics of the underlying (eye regions) data. Entropy minimization is thus functionally compatible with the first operational stage of the RBF classifier, that of clustering, and this explains the improved RBF performance on eye detection. Our experiments on the eye detection task prove the merit of this approach as they show that eye images compressed using optimal wavelet packets lead to improved and robust performance of the RBF classifier compared to the case where original raw images are used by the RBF classifier.
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Karsh, P. K., R. R. Kumar, and S. Dey. "Radial Basis Function-Based Stochastic Natural Frequencies Analysis of Functionally Graded Plates." International Journal of Computational Methods 17, no. 09 (August 7, 2019): 1950061. http://dx.doi.org/10.1142/s0219876219500610.
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This paper deals with portraying the stochastic natural frequencies of cantilever plates made up of functionally graded materials (FGMs) by employing the radial basis function (RBF)-based finite element (FE) approach. The material modeling of FGM plates is carried out by employing three different distribution laws, namely power law, sigmoid law, and exponential law. A generalized algorithm is developed for uncertainty quantification of natural frequencies of the FGM structures due to stochastic variation in the material properties and temperature. The deterministic FE code is validated with the previous literature, whereas convergence study is carried out in between stochastic results obtained from full scale direct Monte Carlo Simulation (MCS) and MCS results obtained from RBF surrogate model of different sample sizes. The percentage of error present in the RBF model is also determined. The influence of crucial parameters such as distribution law, degree of stochasticity, power law index and temperature are determined for natural frequencies analysis of FGMs plates. The results illustrate the input parameters considered in the present study have significant effects on the first three stochastic natural frequencies of cantilever FGM plates.
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Wang, Hong Xiang, and Wen Xian Guo. "Upgrading Water Distribution System Based on GA-RBF Neural Network Model." Advanced Materials Research 267 (June 2011): 605–8. http://dx.doi.org/10.4028/www.scientific.net/amr.267.605.
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Hydraulic network calibration model is to minimize the sum of the squares of the differences between the calibrated and initial pipe roughness estimates, under a set of constraints determined from a sensitivity matrix. The upgrading problem of water distribution system was put forward after the preferable network model was obtained. Radial Basis Function neural network (RBF) based on genetic algorithm (GA) was proposed to solve the model. Genetic algorithm was applied to optimize the parameters of the neural network, and overcome the over-fitting problem. Case study concludes that using Radial Basis Function neural network (RBF) based on genetic algorithm (GA) and good results were obtained.
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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 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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Yu, Jian Li, and Rui Fang Zhou. "Process Monitoring and Adjustment Based on Optimal RBF Network." Applied Mechanics and Materials 336-338 (July 2013): 1286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1286.
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Modern complex manufacturing process output data showed high autocorrelation, resulting in the output of the process to deviate from the design target , or that false alarms increasd in traditional control chart in monitoring process. Statistical Process Control (SPC) and Automatic process control (APC) are main methods of industrial processes. Study based on the optimization of radial basis (Radial Basis Funtion RBF) neural network integrated SPC/APC quality control model, the forecast MMSE controller based on optimal radial basis function networks were utilized to adjut process of productive process output, and residual control charts were utilized to monitor process output after adjustment. Results show that optimal RBF network can improve forecast accuracy and adjustment effect, eliminate effectively process output autocorrelation. The residual control chart will in steady state and with small fluctuation. Intergrated SPC/APC quality control model based on optimal radial basis function can eliminate process fluctuation effectively and guaranteeproduct stable quality in process quality control.
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Feng, Baiwei, Chengsheng Zhan, Zuyuan Liu, Xide Cheng, and Haichao Chang. "Application of Basis Functions for Hull Form Surface Modification." Journal of Marine Science and Engineering 9, no. 9 (September 14, 2021): 1005. http://dx.doi.org/10.3390/jmse9091005.
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Basis functions are key in constructing interpolation equations in hull surface modification based on radial basis functions (RBF) interpolation. However, few have studied the selection of basis functions in depth. By comparing several typical basis functions through a theoretical analysis and two-dimensional modification examples, the Wendland ψ3,1 (W) function is selected. The advantages of hull form surface modification based on W function interpolation are further validated through a case study. Finally, the modification method is used to optimize a trimaran model. An optimal hull form with fair lines is obtained, and its wave-making resistance coefficient and total resistance are reduced by 8.3% and 3.8%, respectively, compared to those of the original model. These findings not only further illustrate that the W function is relatively suitable for hull form surface modification, but also validate the feasibility and value of the RBF interpolation-based surface modification method in engineering practice.
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Sun, Xiao, Shi Fan Qiao, and Ji Ren Xie. "The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China." Applied Mechanics and Materials 641-642 (September 2014): 119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.119.
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Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.
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Duan, Ping, Yehua Sheng, Siyang Zhang, Haiyang Lv, and Jia Li. "DEM Reconstruction Based on Adaptive Local RBF." Open Civil Engineering Journal 8, no. 1 (September 29, 2014): 232–36. http://dx.doi.org/10.2174/1874149501408010232.
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As the core of digital elevation model, interpolation methods have been run through the each link, such as production, quality control, accuracy assessment, analytical applications and etc. The local radial basis function interpolation method based on spatial relationship of natural neighbor was proposed in this paper. The interpolation reference points were chosen by the Delaunay Triangulation. The first-order and second-order neighboring of interpolation points as the interpolation reference points were used to construct local radial basis function. This method was applied to the construction of digital elevation model, and the correspondent errors were analyzed. Experimental result shows that the method has a good effect on the construction of different landform.
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Li, Zhen, Jianping Hao, and Cuijuan Gao. "Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network." Complexity 2021 (November 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/1332242.
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Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.
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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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Tolu, Silvia, Mauricio Vanegas, Rodrigo Agís, Richard Carrillo, and Antonio Cañas. "Dynamics Model Abstraction Scheme Using Radial Basis Functions." Journal of Control Science and Engineering 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/761019.
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This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.
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Sanwale, Jitu, and Dhan Jeet Singh. "Aerodynamic Parameters Estimation Using Radial Basis Function Neural Partial Differentiation Method." Defence Science Journal 68, no. 3 (April 16, 2018): 241. http://dx.doi.org/10.14429/dsj.68.11843.
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Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation of stability and control derivatives from recorded flight data. This problem is extensively studied in the past using classical approaches such as output error, filter error and equation error methods. An alternative approach to these model based methods is the machine learning such as artificial neural network. In this paper, radial basis function neural network (RBF NN) is used to model the lateral-directional force and moment coefficients. The RBF NN is trained using k-means clustering algorithm for finding the centers of radial basis function and extended Kalman filter for obtaining the weights in the output layer. Then, a new method is proposed to obtain the stability and control derivatives. The first order partial differentiation is performed analytically on the radial basis function neural network approximated output. The stability and control derivatives are computed at each training data point, thus reducing the post training time and computational efforts compared to hitherto delta method and its variants. The efficacy of the identified model and proposed neural derivative method is demonstrated using real time flight data of ATTAS aircraft. The results from the proposed approach compare well with those from the other.
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Ahmed, Kamal, Shamsuddin Shahid, and Sobri Harun. "Statistical Downscaling of Rainfall in an Arid Coastal Region: A Radial Basis Function Neural Network Approach." Applied Mechanics and Materials 735 (February 2015): 190–94. http://dx.doi.org/10.4028/www.scientific.net/amm.735.190.
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Downscaling Global Circulation Model (GCM) output is important in order to understand the present climate as well as future climate changes at local scale. In this study, Radial basis function (RBF) neural network was used to downscale the mean monthly rainfall in an arid coastal region located in Baluchistan province of Pakistan. The RBF model was used to downscale monthly rainfall from National Center for environmental prediction (NCEP) reanalysis dataset at four observation stations in the area. The potential predictors were selected using principal component analysis of NCEP variables at grid points located around the study area. Power transformation method was used to remove the bias in the prediction. The results showed that the RBF model was able to establish a good relation between NCEP predictors and local rainfall. The power transformation method was also found to perform well to correct errors in prediction. It can be concluded that RBF and power transformation methods are reliable and effective methods for downscaling rainfall in an arid coastal region.
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Li, Yue, Xiaoquan Chu, Zetian Fu, Jianying Feng, and Weisong Mu. "Shelf life prediction model of postharvest table grape using optimized radial basis function (RBF) neural network." British Food Journal 121, no. 11 (October 24, 2019): 2919–36. http://dx.doi.org/10.1108/bfj-03-2019-0183.
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Purpose The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods. Design/methodology/approach First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data. Findings The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties. Originality/value This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.
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Sujatmiko, Bagus Sayekti, Hermawan Andika, and Timothy John Pattiasina. "Perancangan dan Pembuatan Aplikasi Untuk Mengukur Efektivitas Produksi Berdasarkan Permintaan Pelanggan Dengan Metode Radial Basis Function." Teknika 5, no. 1 (March 9, 2017): 38–42. http://dx.doi.org/10.34148/teknika.v5i1.50.
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Model jaringan saraf fungsi radial basis (Radial Basis Function = RBF) adalah model jaringan saraf yang memiliki unit lapisan tersembunyi, dimana fungsi aktivasinya menggunakan fungsi basis (Gaussian) dan fungsi linear pada lapisan output. Untuk mendapatkan hasil fungsi RBF terbaik, diperlukan kombinasi yang tepat antara jumlah input data dan jumlah node (clustering). Penelitian ini dilakukan diperusahaan kimia yang bergerak dibidang produksi deterjen. Data yang akan diproses diperoleh dari transaksi perusahaan yang sudah dilakukan selama 2 tahun sebelumnya untuk dijadikan sebagai data training dan data testing. Pada data training dilakukan pengelompokan data dan pencarian nilai sentroid menggunakan metode K-Means kemudian dilanjutkan perhitungan RBF sampai menghasilkan nilai bobot training. Hasil bobot training digunakan untuk proses pengujian data testing hingga menghasilkan suatu prediksi produksi berupa nilai similarity. Nilai similarity tertinggi akan digunakan untuk perhitungan prediksi produksi pada aplikasi user. Hasil dari penelitian ini berupa nilai prediksi produksi yang akan digunakan untuk membantu proses pengambilan keputusan dan pemenuhan permintaan pelanggan. Dari percobaan yang sudah dilakukan diperoleh akurasi nilai similarity diatas 90 %.
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Alabi, I. O., and R. G. Jimoh. "Financial Fraud Detection using Radial Basis Network." Circulation in Computer Science 3, no. 1 (January 25, 2018): 10–21. http://dx.doi.org/10.22632/ccs-2017-252-71.
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The ubiquitous cases of abnormal transactions with intent to defraud is a global phenomenon. An architecture that enhances fraud detection using a radial basis function network was designed using a supervised data mining technique― radial basis function (RBF) network, a multivariate interpolation approximation method. Several base models were thus created, and in turn used in aggregation to select the optimum model using the misclassification error rate (MER), accuracy, sensitivity, specificity and receiver operating characteristics (ROC) metrics. The results shows that the model has a zero-tolerance for fraud with better prediction especially in cases where there were no fraud incidents doubtful cases were rather flagged than to allow a fraud incident to pass undetected. Expectedly, the model’s computations converge faster at 200 iterations. This study is generic with similar characteristics with other classification methods but distinct parameters thereby minimizing the time and cost of fraud detection by adopting computationally efficient algorithm.
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Liu, Jichao, Cheng Wang, Peiyu Zhang, Min Gui, Lijia Tong, and Bin Li. "Study on the Nondestructive Measurement of Aluminized Thickness Based on Radial Basis Function Neural Network by X-ray Fluorescence." Coatings 10, no. 8 (August 1, 2020): 754. http://dx.doi.org/10.3390/coatings10080754.
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Aluminizing is a common protective coating for aeroengine turbine blades, but there is no method to accurately measure the aluminized thickness. X-ray fluorescence nondestructive testing technology is a method which can basically realize the measurement of all coatings on the metal substrate. However, the aluminized coating structure is completely different from the conventional coating structure, which causes great difficulties in measuring the aluminized thickness by conventional calculation models. Therefore, to realize the measurement of aluminized thickness, a new modeling method based on radial basis function (RBF) neural network by X-ray fluorescence (XRF) is proposed. By comparing two calculation models of RBF and principal component analysis (PCA)-RBF, the results show that both models can realize the measurement of aluminized thickness, but the accuracy of PCA-RBF is better than that of RBF, and the average relative error of the predicted results is 3.99%; the predicted results of the PCA-RBF model fit the training values better, and its predictability is better.
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Abakar, Khalid AA, and Chongwen Yu. "The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques." International Journal of Emerging Research in Management and Technology 7, no. 1 (June 11, 2018): 72. http://dx.doi.org/10.23956/ijermt.v7i1.25.
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This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.
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He, Bin. "Developing a leap-frog meshless methods with radial basis functions for modeling of electromagnetic concentrator." AIMS Mathematics 7, no. 9 (2022): 17133–49. http://dx.doi.org/10.3934/math.2022943.
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<abstract><p>The main goal of this paper is to develop a fast and effective meshless method by using radial basis function (RBF) for the time domain model equations of electromagnetic wave concentration device. This is mainly because the complex model equations involve different partial differential equations in different subdomains, which makes the meshless method very attractive and also very challenging. In order to simulate the propagation of electromagnetic waves in the electromagnetic concentrator, perfect matching layer technology was used to reduce an unbounded domain problem into a bounded domain problem. Borrowing the idea of the leap-frog finite-difference time-domain scheme, I develop the leap-frog RBF meshless method to solve the coupled complex modeling equations. The numerical results obtained by using a multiquadric RBF and Gaussian RBF demonstrate that our RBF method is very effective.</p></abstract>
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Kalita, Kanak, Shankar Chakraborty, S. Madhu, Manickam Ramachandran, and Xiao-Zhi Gao. "Performance Analysis of Radial Basis Function Metamodels for Predictive Modelling of Laminated Composites." Materials 14, no. 12 (June 15, 2021): 3306. http://dx.doi.org/10.3390/ma14123306.
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High-fidelity structural analysis using numerical techniques, such as finite element method (FEM), has become an essential step in design of laminated composite structures. Despite its high accuracy, the computational intensiveness of FEM is its serious drawback. Once trained properly, the metamodels developed with even a small training set of FEM data can be employed to replace the original FEM model. In this paper, an attempt is put forward to investigate the utility of radial basis function (RBF) metamodels in the predictive modelling of laminated composites. The effectiveness of various RBF basis functions is assessed. The role of problem dimensionality on the RBF metamodels is studied while considering a low-dimensional (2-variable) and a high-dimensional (16-variable) problem. The effect of uniformity of training sample points on the performance of RBF metamodels is also explored while considering three different sampling methods, i.e., random sampling, Latin hypercube sampling and Hammersley sampling. It is observed that relying only on the performance metrics, such as cross-validation error that essentially reuses training samples to assess the performance of the metamodels, may lead to ill-informed decisions. The performance of metamodels should also be assessed based on independent test data. It is further revealed that uniformity in training samples would lead towards better trained metamodels.
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Flyer, Natasha, and Grady B. Wright. "A radial basis function method for the shallow water equations on a sphere." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2106 (April 2009): 1949–76. http://dx.doi.org/10.1098/rspa.2009.0033.
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The paper derives the first known numerical shallow water model on the sphere using radial basis function (RBF) spatial discretization, a novel numerical methodology that does not require any grid or mesh. In order to perform a study with regard to its spatial and temporal errors, two nonlinear test cases with known analytical solutions are considered. The first is a global steady-state flow with a compactly supported velocity field, while the second is an unsteady flow where features in the flow must be kept intact without dispersion. This behaviour is achieved by introducing forcing terms in the shallow water equations. Error and time stability studies are performed, both as the number of nodes are uniformly increased and the shape parameter of the RBF is varied, especially in the flat basis function limit. Results show that the RBF method is spectral, giving exceptionally high accuracy for low number of basis functions while being able to take unusually large time steps. In order to put it in the context of other commonly used global spectral methods on a sphere, comparisons are given with respect to spherical harmonics, double Fourier series and spectral element methods.
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Yavari, H., P. Pahlavani, and B. Bigdeli. "LANDSLIDE HAZARD MAPPING USING A RADIAL BASIS FUNCTION NEURAL NETWORK MODEL: A CASE STUDY IN SEMIROM, ISFAHAN, IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1085–90. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1085-2019.
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Abstract. In this paper, Radial Basis Function (RBF) Neural Network and Logistic Regression (LR) models were proposed for hazard prediction of landslides in a part of the Semirom area (Iran) to compare their accuracy and performance. For this purpose, a spatial database of the study area was prepared that consists of 68 landslide locations and 11 influencing information layers including slope, aspect, profile curvature, plan curvature, distance from faults, distance from roads, distance from residential regions, distance from rivers, land use, lithology and rainfall. Landslide hazard maps were prepared for the study area by applying the proposed algorithms. Performance of the models was assessed using the Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The coefficient of determination (R2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for proposed methods. The outcomes showed that the RBF Neural Network has the highest R2 (0.8224), in comparison to that of the LR model (0.5365). Also, the ROC plots, RMSEs and NRMSEs showed that the proposed RBF Neural Network is much better than the LR model. Consequently, it can be concluded that the RBF Neural Network is the best regression model in this study and it can be considered as a capable method for landslide hazard mapping in landslide-susceptible areas.
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Yao, Jing, Yuanhao Chen, Siyuan Yang, Yuhui Wang, Tao Li, Bo Zhu, Guanyin Cheng, and Xueqin Liu. "A hybrid model with dual channel feature processing for short-term photovoltaic power prediction." Journal of Physics: Conference Series 2247, no. 1 (April 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2247/1/012002.
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Abstract Adverse effects of random fluctuations and intermittent characteristics of solar irradiance usually hamper the proper operation of the photovoltaic power grid. It is therefore desirable to improve the accuracy of photovoltaic (PV) power prediction. In this work, PV forecasting is realized through a Bayesian optimized model which combines the long short-term memory and radial basis function neural network (BOA-LSTM-RBF). The hybrid model presents a dual channel feature processing by extracting the historical data of PV generation via long-short-term memory network (LSTM) and extracting the forecasted weather conditions via radial basis function neural network (RBF). Then the number of hidden layer neurons and the training batch size are simultaneously optimized by & the Bayesian optimization algorithm (BOA). The testing results of three stations demonstrate that, compared with other available models, the RMSE values of BOA-LSTM-RBF model decreased by 2% ∼ 17%, which has striking advantages in prediction precision and generalizability. More interestingly, high-precision PV power forecasting can be achieved even under dramatic weather changes.
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Hage Hassan, Maya, Ghislain Remy, Guillaume Krebs, and Claude Marchand. "Radial output space mapping for electromechanical systems design." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 3 (April 29, 2014): 965–75. http://dx.doi.org/10.1108/compel-05-2013-0192.
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Purpose – The purpose of this paper is to set a relation through adaptive multi-level optimization between two physical models with different accuracies; a fast coarse model and a fine time consuming model. The use case is the optimization of a permanent magnet axial flux electrical machine. Design/methodology/approach – The paper opted to set the relation between the two models through radial basis function (RBF). The optimization is held on the coarse model. The deduced solutions are used to evaluate the fine model. Thus, through an iterative process a residue RBF between models responses is built to endorse an adaptive correction. Findings – The paper shows how the use of a residue function permits, to diminish optimization time, to reduce the misalignment between the two models in a structured strategy and to find optimum solution of the fine model based on the optimization of the coarse one. The paper also provides comparison between the proposed methodology and the traditional approach (output space mapping (OSM)) and shows that in case of large misalignment between models the OSM fails. Originality/value – This paper proposes an original methodology in electromechanical design based on building a surrogate model by means of RBF on the bulk of existing physical model.
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Ebtehaj, Isa, Hossein Bonakdari, and Amir Hossein Zaji. "An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers." Water Science and Technology 74, no. 1 (April 22, 2016): 176–83. http://dx.doi.org/10.2166/wst.2016.174.
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In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (CV) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R2 = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = −0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers.
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Lu, Jinna, Hongping Hu, and Yanping Bai. "Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction." Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/178313.
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This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.
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Sun, Binbin, Tiezhu Zhang, Wenqing Ge, Cao Tan, and Song Gao. "Driving energy management of front-and-rear-motor-drive electric vehicle based on hybrid radial basis function." Archives of Transport 49, no. 1 (March 31, 2019): 47–58. http://dx.doi.org/10.5604/01.3001.0013.2775.
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This paper presents mathematical methods to develop a high-efficiency and real-time driving energy management for a front-and-rear-motor-drive electric vehicle (FRMDEV), which is equipped with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). First of all, in order to develop motor-loss models for energy optimization, database of with three factors, which are speed, torque and temperature, was created to characterize motor operation based on HALTON sequence method. The response surface model of motor loss, as the function of the motor-operation database, was developed with the use of Gauss radial basis function (RBF). The accuracy of the motor-loss model was verified according to statistical analysis. Then, in order to create a two-factor energy management strategy, the modification models of the torque required by driver (Td) and the torque distribution coefficient (β) were constructed based on the state of charge (SOC) of battery and the motor temperature, respectively. According to the motor-loss models, the fitness function for optimization was designed, where the influence of the non-work on system consumption was analyzed and calculated. The optimal β was confirmed with the use of the off-line particle swarm optimization (PSO). Moreover, to achieve both high accuracy and real-time performance under random vehicle operation, the predictive model of the optimal β was developed based on the hybrid RBF. The modeling and predictive accuracies of the predictive model were analyzed and verified. Finally, a hardware-in-loop (HIL) test platform was developed and the predictive model was tested. Test results show that, the developed predictive model of β based on hybrid RBF can achieve both real-time and economic performances, which is applicable to engineering application. More importantly, in comparison with the original torque distribution based on rule algorithm, the torque distribution based on hybrid RBF is able to reduce driving energy consumption by 9.51% under urban cycle.