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Evolution of radial basic function neural network for fast restoration of distribution systems with load variations

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第4期33卷 961-968页

作者： Huang, Chao-Ming Hsieh, Cheng-Tao Wang, Yung-Shan Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan

This paper proposes a new algorithm to construct the optimal radial basic function (RBF) neural network for fast restoration of distribution systems with load variations. Service restoration of distribution systems is to restore power to the blacked out but unfaulted area. basically, it is a stressful and urgent task that must be performed by system operators. In this paper, a new algorithm which combines orthogonal least-squares (OLS) and enhanced differential evolution (EDE) methods is developed to construct the optimal RBF network that shall further achieve the fast restoration of distribution systems. The proposed scheme comprises training data creation phase and network construction phase. In the training data creation phase, a heuristic-based fuzzy inference (HBFI) method is employed to build the restoration plans under various load levels. Then an optimal RBF network is constructed by OLS and EDE algorithms in the network construction phase. Once the RBF network is constructed properly, the desired restoration plan can be produced as soon as the inputs are given. The proposed method was tested on a typical distribution system of the Taiwan Power Company (TPC). Results show that the proposed method outperforms the existing methods in terms of convergence performance and forecasting accuracy. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： radial basic function Orthogonal least-squares Enhanced differential evolution Service restoration Distribution engineering

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Implementation of radial basic function networks for the prediction of RO membrane performances by using a complex transport model

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DESALINATION AND WATER TREATMENT 2016年第43期57卷 20307-20317页

作者： Iranmanesh, Farshid Moradi, Ali Rafizadeh, Mehdi Shahid Bahonar Univ Kerman Dept Chem Engn POB 76175-133 Kerman Iran Iran Polymer & Petrochem Inst Petrochem Dept POB 115-14965 Tehran Iran Amirkabir Univ Technol Polymer & Color Engn Dept Tehran Iran

The main purpose of this research was the prediction of reverse osmosis (RO) membranes performances, including separation factor, pure solvent flux, and total flux. The modified surface force-pore flow is a mathematical and mechanism base model that has predicated the performances of RO membranes appropriately better than others. The equations of this model are complex and nonlinear, which should be solved by an advanced numerical method eventually. In this research, the radial basic function (RBF) network, which is an exclusive typical of artificial neural network (ANN) and an association of radial functions into a single hidden layer, was implemented for the predication of RO membrane performances. The input data of the network were the parameters of this mathematical model and some experimental data which had been obtained from four different types of RO membranes. About 80% of the total experimental data were implemented for the training of data-set and the remaining 20% were used for the testing of data-set. The predictive ability of RBF network was evaluated correctly by the mean square error, root mean square error, and correlation coefficient (R). The mean square errors for training and for testing data-set consequently were obtained 0.00009 and 0.00016 (for separation factor), 0.00013 and 0.00013 (for pure solvent flux), and 0.00009 and 0.00012 (for total flux), respectively. The results showed that this technique has predicted the performances of RO membrane more correctly vs. the experimental data comparing to the previous ANNs and comparing to original model, that is, modified surface force pore flow model accordingly.

关键词： Artificial neural network MD-SF-PF model radial basic function RO membrane Performances radial basic function

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A Hybrid Bacterial Foraging Optimization Algorithm and a radial basic function Network for Image Classification

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2017年第2期13卷 215-235页

作者： Amghar, Yasmina Teldja Fizazi, Hadria Univ Sci & Technol Oran Mohamed Boudiaf Dept Comp Sci Fac Math & Comp Sci Oran Algeria

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

关键词： Bacterial Foraging Optimization Algorithm Hybrid Image Classification radial basic function

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Spatial Prediction of Landslide Hazard at the Yihuang Area (China): A Comparative Study on the Predictive Ability of Backpropagation Multi-layer Perceptron Neural Networks and radial basic function Neural Networks 27th

Spatial Prediction of Landslide Hazard at the Yihuang Area (...

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27th International Cartographic Conference (ICC)

作者： Hong, Haoyuan Xu, Chong Revhaug, Inge Bui, Dieu Tien China Earthquake Adm Inst Geol Key Lab Act Tecton & Volcano 1 HuayanliPOB 9803 Beijing 100029 Peoples R China Jiangxi Meteorol Bur Jiangxi Prov Meteorol Observ Nanchang 330046 Peoples R China Norwegian Univ Life Sci Dept Math Sci & Technol N-1432 As Norway Telemark Univ Coll Fac Arts & Sci Dept Business Adm & Comp Sci N-3800 Bo Telemark Norway

ISBN: (纸本)9783319177380;9783319177373

The aim of this study is to investigate potential applications of multi-layer perceptron neural networks (MLP Neural Nets) and radial basis function neural networks (RBF Neural Nets) for landslide susceptibility mapping in the Yihuang area (China). First, a landslide inventory map with 187 landslide locations was generated, and then the map was randomly partitioned into a ratio of 70/30 for training and validating models. Second, 14 landslide conditioning factors (slope, altitude, aspect, topographic wetness, sediment transport index (STI), stream power index (SPI), plan curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), lithology, rainfall) were prepared. Using MLP Neural Nets and RBF Neural Nets, two landslide susceptibility models were constructed and two landslide susceptibility maps were generated. Finally, the two resulting landslide susceptibility maps were validated using the landslide locations and the receiver operating characteristic (ROC) method. The validation results showed that the areas under the ROC curve (AUC) for the two landslide models produced by MLP Neural Nets and RBF Neural Nets are 0.932 and 0.765 for success rate curve and 0.757 and 0.725 for prediction rate curve, respectively. The results showed that the MLP Neural Nets model is better than the RBF Neural Nets model in this study. The results may be useful for general land use planning and hazard mitigation purposes.

关键词： Neural networks radial basic function Landslide

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Evolving radial basic function Neural Network for Fast Restoration of Distribution Systems

Evolving Radial Basic Function Neural Network for Fast Resto...

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6th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Huang, Yann-Chang Chen, Shin-Ju Huang, Chao-Ming Cheng Shiu Univ Dept Elect Engn Kaohsiung 833 Taiwan Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan

ISBN: (纸本)9781424487554

This paper presents an optimal radial basic function (RBF) neural network for fast restoration of distribution systems under different load levels. basically, service restoration of distribution systems is a stressful and urgent task that must be performed by system operators. In this paper, a RBF network evolved by an enhanced differential evolution (EDE) algorithm is developed to achieve the fast restoration of distribution systems. The proposed scheme comprises training data creation phase and network construction phase. In the training data creation phase, a heuristic-based fuzzy inference (HBFI) method is employed to build the restoration plans under various load levels. Then an optimal RBF network is constructed by the EDE algorithm in the network construction phase. Once the RBF network is constructed properly, the desired restoration plan can be produced as soon as the inputs are given. The proposed method has been verifiedd on a typical distribution system of the Taiwan Power Company (TPC). Results show the proposed method can provide better convergence performance and forecasting accuracy than the existing methods.

关键词： radial basic function differential evolution service restoration distribution engineering

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radial basic function-BASED ANALYSIS OF DYNAMIC DEFLECTION OF INVISIBLE FLEXIBLE PAVEMENT LAYER PROFILES

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ENGINEERING REVIEW 2013年第1期33卷 65-74页

作者： Yin, F. C. Zhang, Y. Zhou, G. C. Guo, H. Harbin Inst Technol Sch Civil Engn Harbin 150090 Heilongjiang Peoples R China SRA Atlantic City NJ 08401 USA

This study proposes a radial basic function (RBF) neural network model which can simulate the dynamic deflection process of invisible individual layers in the full-scale flexible pavement along with an increase of load repetitions. The training and testing data is formed through empirical and conceptual judgment on the final profiles of the four pavement layers in the test. The independent and dependent variables are defined as the known top and invisible layer deflections respectively. Then, the RBF model produces the numerical results between layer dynamic deflections. Finally, several parameters are suggested to study the response of the invisible pavement layers. The RBF model shows that the implicit dynamic relationship between pavement layer deflections could be modeled by a static state of the flexible pavement. Furthermore, some working features of the pavement might be revealed from its dynamic response.

关键词： Flexible pavement Layer deflection Neural network radial basic function Dynamic

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Acoustic measurement of velocity filed using improved radial basic function neural network

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2023年 202卷

作者： Kong, Qian Lu, Ying Jiang, Genshan Liu, Yuechao North China Elect Power Univ Dept Math & Phys Baoding 071003 Peoples R China North China Elect Power Univ Sch Math & Phys Beijing 102206 Peoples R China

In coal-fired boilers, the aerodynamic field directly affects the combustion conditions of the boiler and safety of the furnace. In this work, a new method based on acoustic tomography (AT) is proposed to measure the velocity field by using radial basis function neural network (RBFNN). First, we establish the proposed RBFNN model to reconstruct the velocity field and study the influence of various parameters in-cluding the number and arrangement of sensors, the shape parameters and the number of center points in RBF, and the hyper parameters in the neural network to improve the reconstruction performance. Sec-ond, a novel improved RBFNN acoustic algorithm is used to reconstruct different two-dimensional veloc-ity fields based on numerical simulations. The reconstruction results obtained using the proposed algo-rithms have higher accuracy, better real-time performance, and noise immunity as compared with other classical acoustic algorithms. Third, the physical experiments are conducted in lab to further assess the proposed algorithm. The results exhibit that the proposed RBFNN acoustic method provided satisfactory consistency as compared to the traditional measurement approaches. This proves the effectiveness of the proposed acoustic method in practical velocity field measurement.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Acoustic method Velocity filed measurement Neural network radial basic function Furnace

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Prediction of ketosis using radial basis function neural network in dairy cattle farming

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PREVENTIVE VETERINARY MEDICINE 2025年 235卷 106410页

作者： Bauer, Edyta A. Jagusiak, Wojciech Agr Univ Krakow Fac Anim Sci Dept Anim Reprod Anat & Genom Al Mickiewicza 24-28 PL-30059 Krakow Poland Agr Univ Krakow Fac Anim Sci Dept Genet Anim Breeding & Ethol Al Mickiewicza 24-28 PL-30059 Krakow Poland

The purpose of the paper was to apply an Artificial Neural Networks with radial Basis function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes. The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows. The milk samples were collected during test-day milkings and made available by Polish Federation of Cattle Breeders and Milk Producers. The milk composition parameters were used as the input variables for RBF network models. The value of the output variable was determined based on the content of beta-hydroxybutyric acid in blood of cows. In the next stage of the work, the qualities of the pre-selected models were compared and the best ones were chosen. The sensitivity and specificity as well as the size of the AUC (Area Under the Curve) under the ROC (Receiver Operating Characteristic) were taken as the main criteria for network models evaluation. The model characterized by sensitivity of 0.86, specificity of 0.71 and AUC of 0.89 was selected for ketosis type I. The optimal for ketosis type II showed the sensitivity and specificity 0.81 and 0.75, respectively, and the size of AUC above 0.85. Chosen models were recorded using the predictive modelling markup language (PMML) for data mining models to be shared and used between the different applications.

关键词： Artificial neural networks radial basic function beta-hydroxybutanoic acid Ketosis Dairy farming

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Special functions for the extended calibration of charge-mode accelerometers

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2021年 71卷 153-169页

作者： Tomczyk, Krzysztof Piekarczyk, Marcin Sieja, Marek Sokal, Grzegorz Cracow Univ Technol Fac Elect & Comp Engn Warszawska 24 S PL-31155 Krakow Poland Pedag Univ Krakow Fac Exact & Nat Sci 2 Podchorazych Ave PL-30084 Krakow Poland

In this paper, we propose a new procedure for the extended calibration of charge-mode accelerometers. This procedure covers two main stages. The first one follows the general guidelines of international guides that relate to calibrating accelerometers in the frequency domain and are implemented using a weighted-least-squares method. These guidelines are modified herein for the advanced modelling of the charge-mode accelerometer. The second stage provides a new solution which is an extension of the standard calibration using the upper bound of the dynamic error (UBDE) according to the integer-square criterion and a fixed-point algorithm that enables this error to be determined. Additionally, the special functions, representing the values of the dynamic error for the predetermined ranges of the parameters associated with the mathematical model of the charge-mode accelerometer, are determined. Such functions ensure an easy and rapid execution of the second stage of calibration without conducting complicated procedures to determine the value of the UBDE. The extended procedure proposed here enables the comparison of accelerometers with similar technical parameters, but produced by different manufacturers of measuring sensors that frequently compete with each other. In this way, we can select the accelerometer that will produce the lower value of UBDE.

关键词： Accelerometer calibration Integral-square error radial basic function

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A new method for removal of powerline interference in ECG and EEG recordings

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COMPUTERS & ELECTRICAL ENGINEERING 2015年 45卷 235-248页

作者： Mateo, J. Sanchez-Morla, E. M. Santos, J. L. Univ Castilla La Mancha Innovat Bioengn Res Grp Cuenca Spain Hosp Univ Guadalajara Dept Psychiat Guadalajara Spain Alcala Univ Sch Med Madrid Spain Virgen Luz Hosp Clin Psychiat Serv Cuenca Spain

Advanced medical diagnosing and research requires precise information which can be obtained from measured electrophysiological data, e.g., electroencephalogram (EEG) and electrocardiograph (ECG). However, they are often contaminated with noise and a variety of bioelectric signals called artefacts, e.g., electromyography (EMG). These noise and artefacts which need to be reduced make it difficult to distinguish normal from abnormal physiological activity. Electromagnetic contamination of recorded signals represents a major source of noise in electrophysiology and impairs the use of recordings for research. In this paper we present an effective method for cancelling 50 Hz (or 60 Hz) interference using a radial basis function (RBF) Wiener hybrid filter. One of the main points of this paper is the hybridization of the RBF filter and a Wiener filter to make full use of both merits. The effectiveness and validity of those filters are verified by applying them to ECG and EEG recordings. The results show that the proposed method is able to reduce powerline interference (PLI) from the noisy ECG and EEG signals more accurately and consistently in comparison to some of the state of-the-art methods and this method can be efficiently used with very low signal-to-noise ratios, while preserving original signal waveform. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Electromagnetic interference Powerline Biomedical signals radial basic function Wiener filter

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