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radial basis function neural network

Kelly de Jesus, Karla de Jesus, Helon Vicente Hultmann Ayala, Leandro Dos Santos Coelho, João Paulo Vilas-Boas, Ricardo Jorge Pinto Fernandes
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities using physiological and biomechanical dataset. Ten trained male swimmers completed a 7 × 200 m front crawl protocol (0.05 m.s-1 increments and 30 s intervals) to assess expiratory gases and blood lactate concentrations. Two surface and four underwater cameras recorded independent images subsequently processed focusing a three-dimensional reconstruction of two upper limb cycles at 25 and 175 m laps...
February 6, 2019: Journal of Sports Sciences
Ce Shi, Jianping Qian, Wenying Zhu, Huan Liu, Shuai Han, Xinting Yang
This study develops a reliable radial basis function neural networks (RBFNNs) to estimate freshness for tilapia fillets stored under non-isothermal conditions by using optimal wavelengths from hyperspectral imaging (HSI). The results show that, for tilapia fillet stored at -3, 0, 4, 10, and 15 °C and non-isothermal conditions, total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and the K value increase whereas sensory scores decrease with increasing storage time. To simplify the models, nine optimal wavelengths were selected by using the successive projections algorithm (SPA), following which SPA-RBFNN models were built based on the selected wavelengths and the values of TVB-N, TAC, K, and sensory evaluations for tilapia fillets store isothermally...
March 1, 2019: Food Chemistry
Xiang Shen, Hongfei Zhu, Jiabao Jiang, Yongquan Deng, Song Ji
PURPOSE: Recent studies suggested that suboptimal delivery and longitudinal stent deformation can result in in-stent restenosis. Therefore, the purpose of this paper was to study the effect of stent geometry on stent flexibility and longitudinal stiffness (LS) and optimize the two metrics simultaneously. Then, the reliable and accurate relationships between metrics and design variables were established. METHODS: A multi-objective optimization method based on finite element analysis was proposed for the investigation and improvement of stent flexibility and LS...
January 23, 2019: Cardiovascular Engineering and Technology
Salim Lahmiri, Debra Ann Dawson, Amir Shmuel
Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms...
February 2018: Biomedical Engineering Letters
Shiwen Zhang, Qiang Shen, Chaojia Nie, Yuanfang Huang, Jianhua Wang, Qingqing Hu, Xuejiao Ding, Yan Zhou, Yuanpeng Chen
Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands...
December 18, 2018: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
Youzhi Zhang, Jinhua Ye, Zhengkang Lin, Shuheng Huang, Haomiao Wang, Haibin Wu
Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element...
December 21, 2018: Sensors
M Fooladi, H Sharini, S Masjoodi, A Khodamoradi
Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter, then the performance of three ANN-based classifiers have been investigated...
December 2018: Journal of Biomedical Physics & Engineering
Rajesh Kumar, Smriti Srivastava, J R P Gupta, Amit Mohindru
In this paper, a novel temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems is proposed. The proposed structure consists of recurrent hidden neurons having weighted self-feedback loops and a weighted linear feed-through from the input layer directly to the output layer neuron(s). The dynamic back-propagation algorithm is developed and used for updating the parameters of the proposed structure. To improve the performance of learning algorithm, discrete Lyapunov stability method is used to develop an adaptive learning rate scheme...
December 4, 2018: ISA Transactions
Nada M Moawad, Wael M Elawady, Amany M Sarhan
In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted...
November 24, 2018: ISA Transactions
Daisuke Nagasato, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, Masahiro Kameoka, Masanori Niki, Ken Hayashi, Yoshinori Mitamura
The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients ( n =125 images) and 202 non-CRVO normal subjects ( n =238 images) were included in this study. Training to construct the DL model using deep convolutional neural network algorithms was provided using ultrawide-field fundus images. The SVM uses scikit-learn library with a radial basis function kernel...
2018: Journal of Ophthalmology
Oliver Klein, Frederic Kanter, Hagen Kulbe, Paul Jank, Carsten Denkert, Grit Nebrich, Wolfgang D Schmitt, Zhiyang Wu, Catarina A Kunze, Jalid Sehouli, Silvia Darb-Esfahani, Ioana Braicu, Jan Lellmann, Herbert Thiele, Eliane T Taube
PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study was to examine the potential of MALDI-Imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. EXPERIMENTAL DESIGN: Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas and 14 serous borderline tumors were analysed using MALDI-Imaging...
November 24, 2018: Proteomics. Clinical Applications
Wasim Raza, Sang-Bum Ma, Kwang-Yong Kim
In order to maximize the mixing performance of a micromixer with an integrated three-dimensional serpentine and split-and-recombination configuration, multi-objective optimizations were performed at two different Reynolds numbers, 1 and 120, based on numerical simulation. Numerical analyses of fluid flow and mixing in the micromixer were performed using three-dimensional Navier-Stokes equations and convection-diffusion equation. Three dimensionless design variables that were related to the geometry of the micromixer were selected as design variables for optimization...
March 4, 2018: Micromachines
Di Wang, Xiaosu Xu, Yongyun Zhu
In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment...
November 10, 2018: Sensors
Lei Feng, Susu Zhu, Chu Zhang, Yidan Bao, Pan Gao, Yong He
Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874⁻1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated...
November 8, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
Ateeq-Ur-Rauf, Abdul Razzaq Ghumman, Sajjad Ahmad, Hashim Nisar Hashmi
Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another...
November 8, 2018: Environmental Monitoring and Assessment
M E Karar, M A El-Brawany
Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) neural network controller for high-temperature hyperthermia thermal dose during the therapeutic procedure of cancer tumours by short-time pulses of high-intensity focused ultrasound (HIFU). The developed controller is stabilized and automatically tuned based on Lyapunov functions and ant colony optimization (ACO) algorithm, respectively. In addition, this thermal dose control system has been validated using one-dimensional (1-D) biothermal tissue model...
November 8, 2018: Network: Computation in Neural Systems
Yongduan Song, Liu He, Dong Zhang, Jiye Qian, Jin Fu
This paper investigates the position and attitude tracking control problem of a quadrotor unmanned aerial vehicle subject to modeling uncertainties and actuator failures. A comprehensive mathematical model reflecting the nonlinearity and state-space coupling of the dynamics as well as actuation faults and external disturbances is derived. By combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, an indirect NN-based adaptive fault-tolerant control scheme is developed, which exhibits several attractive features as compared with most existing methods: 1) it is not only robust and adaptive to nonparametric uncertainties but also tolerant to unexpected actuation faults; 2) it ensures stable tracking without the need for precise information on system model; and 3) it only involves one lumped parameter adaptation, thus is structurally simpler and computationally less expensive, rendering the resultant scheme less demanding in programming and more affordable for onboard implementation...
November 5, 2018: IEEE Transactions on Neural Networks and Learning Systems
Ting Wang, Lili Tang, Feng Luan, M Natália D S Cordeiro
Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure⁻activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively...
October 31, 2018: International Journal of Molecular Sciences
Sebastian Roldan-Vasco, Sebastian Restrepo-Agudelo, Yorhagy Valencia-Martinez, Andres Orozco-Duque
Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles could lead to dysphagia. However, there is a lack of standardized and non-invasive methods that support and improve the diagnosis and ambulatory care. This paper presents a classification scheme of two swallowing phases (oral and pharyngeal) based on signals of surface electromyography (sEMG). Eight acquisition channels recorded the EMG activity of 47 healthy subjects while they swallowed water, yogurt and saliva...
December 2018: Journal of Electromyography and Kinesiology
Ireneusz Zagórski, Mariusz Kłonica, Monika Kulisz, Katarzyna Łoza
This paper investigates the effect of change of the abrasive flow rate and the jet feed on the effectiveness of machining of AZ91D casting magnesium alloy. The evaluation of the state of the workpiece surface was based on surface and area roughness parameters (2D and 3D), which provided data on: irregularities formed on the workpiece edge surface (water jet exit), the surface quality after cutting, the workpiece surface chamfering, microhardness of the machined surface, and of specimen cross-sections (along the water jet impact)...
October 26, 2018: Materials
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