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8th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Yuan, Li Chen, Junlin Hainan Med Univ Dept Phys Sci Haikou 571179 Hainan Peoples R China Hainan Univ Dept Lib Haikou 570228 Hainan Peoples R China

ISBN: (纸本)9781538604977

Internet of Things is challenging with the updating human-based terminals, including Wearable Intelligent Monitoring, in which Electromyography (EMG) is the fundamental research discipline. Different human physical activities in various circumstances produce different EMG signals from head to feet. In this paper, we built the prediction model based on radial basis function neural networks to simulate the active pattern of EMG characteristics from both experiment and simulation. Results indicates that the relative prediction error is well controlled within 5.0%, and is stable in more than 98.5% instances.

关键词： EMG identification radial basis function neural networks nonlinear approximation

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Robust Position Control of Electro-Hydrostatic Actuator Systems with radial basis function neural networks

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2013年第2期7卷 257-267页

作者： Choi, Jeongju Dong A Univ Res Fdn Ind Acad Cooperat Pusan South Korea

This paper presents the robust tracking control of EHA (Electro Hydrostatic Actuator) system. EHA system provides the simple structure and frees from the environment pollution, but the poor tracking performance is caused by the internal leakage, surging of pump and friction. In order to increase the tracking performance of EHA system, ABSC (Adaptive Back-Stepping Control) with RBFNN (radial basis function neural networks) is proposed. The proposed control scheme can guarantee the robustness and increase the tracking performance in accordance with the external disturbance and unmodeled uncertainties. The update law for the proposed ABSC with RBFNN is designed based on the reconstruction error. To verify the effectiveness of the proposed control scheme, computer simulation and experiments are executed and the results were compared with that of the general BSC(Back-Stepping Control).

关键词： Electro-Hydrostatic Actuator Friction Adaptive Back-Stepping Control radial basis function neural networks Artificial Intelligent

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A Dual Filter Based on radial basis function neural networks and Kalman Filters with Application to Numerical Wave Prediction Models

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SENSORS 2024年第24期24卷 8006页

作者： Donas, Athanasios Kordatos, Ioannis Alexandridis, Alex Galanis, George Famelis, Ioannis Th. Univ West Attica Dept Elect & Elect Engn Ancient Olive Grove Campus 250Thivon Ave Egaleo 12241 Athens Greece Hellenic Naval Acad Hatzikiriakion 18539 Piraeus Greece

The aim of this study is to introduce and evaluate a dual filter that combines radial basis function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal radial basis function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter's potential as a robust post-processing tool for environmental simulations.

关键词： Kalman filters post-process algorithms radial basis function neural networks significant wave height WAM

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Protection scheme for EHV transmission systems with thyristor controlled series compensation using radial basis function neural networks

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ELECTRIC MACHINES AND POWER SYSTEMS 1997年第5期25卷 553-565页

作者： Song, YH Xuan, QY Johns, AT School of Electronic and Electrical Engineering The University of Bath Bath BA2 7AY United Kingdom

Since the complex variation of line impedance measured is controlled by thyristors and is accentuated as the capacitor's own protection equipment operates randomly under fault conditions in controllable series compensated transmission systems (CSC), conventional distance protection schemes are limited to certain applications. The authors have extensively addressed the development of new protection techniques for such systems using multilayer percetrons. The basic idea of the method is to design a protection scheme using a neural network approach by catching the feature signals in a certain frequency range under fault conditions. This is different from conventional schemes that are based on deriving implicit mathematical equations based on the information obtained by complex filtering techniques. This paper presents some recent results of employing radial basis function neural networks (RBFN) for this particular application. The use of RBFN is because it has a number of advantages over multilayer percetrons. The study shows that the RBFN based protection works well in CSC systems under a number of system and fault conditions.

关键词： Facts Power system protection radial basis function neural networks

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Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第3期34卷

作者： Bose, Anandh Sam Chandra Srinivasan, C. Surenderanath, S. AlMaarefa Univ Coll Appl Sci Dept Ind Engn Riyadh Saudi Arabia Saveetha Engn Coll Dept ECE Chennai India Sri Sairam Inst Technol Dept ECE Chennai Tamil Nadu India

Rheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x-ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer-independent computer-aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed-forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray-level co-occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate.

关键词： artificial neural network (ANN) convolutional neural network (CNN) nonlinear isotropic PDE radial basis function neural networks rheumatoid arthritis wiener filter

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Estimations of Vertical Rail Bending Moments from Numerical Track Deflection Measurements Using Wavelet Analysis and radial basis function neural networks

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2021年第2期147卷

作者： Do, Ngoan Tien Gul, Mustafa Univ Alberta Dept Civil & Environm Engn Markin CNRL Nat Resources Engn Facil 5-075 Edmonton AB T6G 2W2 Canada Univ Alberta Dept Civil & Environm Engn Donadeo Innovat Ctr Engn 7-2579211 16 St NW Edmonton AB T6G 1H9 Canada

A method for estimating rail bending moments from relative vertical track deflection data measured by a train-mounted measurement system is presented in this paper. The novelty of the current study is that complete estimations of rail positive and negative bending moments from track deflection measurements are conducted by using a wavelet multiresolution analysis in conjunction with the radial basis function neural network considering the effects of varying track modulus. The simulation results show that the proposed framework can effectively employ vertical track deflections to estimate both maximum positive and negative bending moments in rails, with the average estimation error being 6.22% (i.e., 2.82 kNm). Moreover, the study confirms the capability of the train-mounted vertical track deflection measurement system (commercially known as MRail) in evaluating the rail bending moments over long distances.

关键词： Railway track Continuous deflection measurement systems Track modulus Rail bending moments Wavelet analysis radial basis function neural networks

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Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces

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JOURNAL OF FOOD ENGINEERING 2021年 298卷

作者： Xiong, Yating Ohashi, Shintaroh Nakano, Kazuhiro Jiang, Weizhong Takizawa, Kenichi Iijima, Kazuyuki Maniwara, Phonkrit Niigata Univ Grad Sch Sci & Technol Nishi Ku 8050 Ikarashi 2 No Cho Niigata 9502181 Japan Niigata Univ Fac Agr Nishi Ku 8050 Ikarashi 2 No Cho Niigata 9502181 Japan China Agr Univ Coll Water Resources & Civil Engn Qinghua Donglu 17 Beijing 100083 Peoples R China Niigata Univ Management Fac Tourism Management 2909-2 Kibougaoka Kamo Niigata 9591321 Japan Chiang Mai Univ Postharvest Technol Res Ctr Fac Agr 239 Huaykaew Rd Chiang Mai 50200 Thailand

This study was carried out to evaluate the feasibility of using Vis/near-infrared (Vis/NIR) spectroscopy for determining the potassium concentration in fresh lettuce leaves and petioles of single-variety lettuce and mixed lettuce leaves of two varieties. Partial least squares (PLS) and radial basis function (RBF) neural network were systemically studied and compared as regressions tools in developing the prediction models. Competitive adaptive reweighted sampling (CARS) variable selection and spectral preprocessing (first- and second-order derivatives) were applied to optimize the performance of predictions. On the basis of these selected optimum wavelengths, the established PLS prediction models provided the coefficients of determination (R-2) of 0.83 and 0.71, residual predictive deviations (RPD) were 1.95 and 1.80, and root mean square errors of prediction (RMSEP) were 39.07 and 38.06 mg/100 g for green leaves and petioles, respectively. By comparison, the RBF approach with first-derivative preprocessing spectra was found to provide the best performance of mixed samples, yielding R-2 of 0.86 and 0.88, RMSEP of 31.20 and 27.63 mg/100 g, and RPD of 2.44 and 2.47 for green leaves and petioles, respectively. The overall results of this study revealed the potential for use of Vis/NIR spectroscopy as an objective and non-destructive method to inspect the potassium concentration of fresh lettuces.

关键词： Nondestructive determination Fresh lettuces Potassium concentration Visible/near-infrared spectroscopy radial basis function neural networks

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Enhanced Image Reconstruction in Electrical Impedance Tomography using radial basis function neural networks

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MEASUREMENT SCIENCE REVIEW 2024年第6期24卷 200-210页

作者： Nomvussi, Serge Ayme Kouakouo Mikulka, Jan Brno Univ Technol Dept Theoret & Expt Elect Engn Antoninska 548-1 Brno 60190 Czech Republic

This paper presents a novel cascade algorithm for image reconstruction in electrical impedance tomography (EIT) using radial basis function neural networks. The first subnetwork applies a density-based algorithm and k-nearest neighbors (KNN) to determine the center and width of the radial basis function neural networks, with the aim of preventing ill-conditioned connection weights between the hidden and output layers. The second subnetwork is a generalized regression neural network dedicated to functional approximation. The combined subnetworks result in a reduced mean square error and achieve an accuracy of 89.54 % without noise and an accuracy between 82.90 % and 89.53 % with noise levels ranging from 30 to 60 dB. In comparison, the original radial basis function neural networks (RBFNN) method achieves an accuracy of 85.44 % without noise and between 80.90 % and 85.31 % under similar noise conditions. The total variation (TV) method achieves 83.13 % without noise, with noise-influenced accuracy ranging from 34.28 % to 45.15 %. The Gauss-Newton method achieves 82.35 % accuracy without noise, with accuracy ranging from 33.21 % to 46.15 % in the presence of noise. The proposed method proves to be resilient to various types of noise, including white Gaussian noise, impulsive noise, and contact noise, and consistently delivers superior performance. It also outperforms the other methods in noise-free conditions. The reliability of the method in noisy environments supports its potential application in the development of new modular systems for electrical impedance tomography.

关键词： Artificial neural network density-based algorithm electrical impedance tomography k-nearest neighbors radial basis function neural networks EIDORS framework

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Trip Attraction Model Using radial basis function neural networks

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Procedia Engineering 2015年 125卷 445-451页

作者： Joni Arliansyah Yusuf Hartono Department Of Civil Engineering University of Sriwijaya Inderalaya Sumatera Selatan 30662 Indonesia Department Of Math Education University of Sriwijaya Inderalaya Sumatera Selatan 30662 Indonesia

Trip Attraction model with seven independent variables, i.e., population size, number of schools, number of students, number of teachers, areas of school buildings, number of offices, and number of houses applying radial basis function neural networks (RBFNN) is presented in this paper. The data used in this study were derived from the origin destination survey in Palembang and the model was developed using 85 sets of land use - trip attraction data. A comparison was made between RBF model and regression model. The results show that RBF model performs better than regression model in predicting trip attraction and important variables are number of students, number of teachers, total areas of school buildings and number of offices.

关键词： Trip Attraction Prediction radial basis function neural networks

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NORM-BASED LOCALIZED GENERALIZATION ERROR MODEL AND ITS DERIVATION FOR radial basis function neural networks

NORM-BASED LOCALIZED GENERALIZATION ERROR MODEL AND ITS DERI...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： XI-ZHAO WANG XIAO-YAN LIU YAN LI CHUN-GUO LI Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding 071002 China

In pattern classification problems, the generalization error is often defined as the integral of a square function for the entire input *** this paper, a new localized generalization error model is proposed for radial basis neural networks, which computes the generalization error within a neighborhood of the training samples based on a given *** with the traditional Mean Square Error term, it is constructed in a more universal perspective way and becomes simpler in calculation for multiple classification ***, the derivation formula of applying this model for radial basis function neural network is obtained by using stochastic sensitivity measure.

关键词： Localized generalization error Stochastic sensitivity Norm radial basis function neural networks

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