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radial basis function neural network

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
Nikolaos Passalis, Anastasios Tefas
Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs are complex models that require the use of powerful hardware, both for training and deploying them. To this end, a quantization-based pooling method is proposed in this paper. The proposed method is inspired from the bag-of-features model and can be used for learning more lightweight deep neural networks. Trainable radial basis function neurons are used to quantize the activations of the final convolutional layer, reducing the number of parameters in the network and allowing for natively classifying images of various sizes...
October 24, 2018: IEEE Transactions on Neural Networks and Learning Systems
Jang-Hyun Park, Seong-Hwan Kim, Tae-Sik Park
A novel adaptive neural output-feedback controller for SISO nonaffine pure-feedback nonlinear systems is proposed. The majority of the previously described adaptive neural controllers for pure-feedback nonlinear systems were based on the dynamic surface control (DSC) or backstepping schemes. This makes the control law as well as the stability analysis highly lengthy and complicated. Moreover, there has been very limited research till date on the output-feedback neural controller for this class of the systems...
October 1, 2018: IEEE Transactions on Neural Networks and Learning Systems
Zijun Ren, Wenxing Fu, Supeng Zhu, Binbin Yan, Jie Yan
Inspired by the exceptional flight ability of birds and insects, a bio-inspired neural adaptive flight control structure of a small unmanned aerial vehicle was presented. Eight pressure sensors were elaborately installed in the leading-edge area of the forward wing. A back propagation neural network was trained to predict the aerodynamic moment based on pressure measurements. The network model was trained, validated, and tested. An adaptive controller was designed based on a radial basis function neural network...
September 26, 2018: Sensors
Takao Yamasaki, Shozo Tobimatsu
Driving requires multiple cognitive functions including visuospatial perception and recruits widespread brain networks. Recently, traffic accidents in dementia, particularly in Alzheimer disease spectrum (ADS), have increased and become an urgent social problem. Therefore, it is necessary to develop the objective and reliable biomarkers for driving ability in patients with ADS. Interestingly, even in the early stage of the disease, patients with ADS are characterized by the impairment of visuospatial function such as radial optic flow (OF) perception related to self-motion perception...
2018: Frontiers in Neurology
Cui-Hua Zhang, Guang-Hong Yang
This paper investigates the event-triggered adaptive output feedback control problem for a class of uncertain nonlinear systems in the presence of actuator failures and unknown control direction. By utilizing the adaptive backstepping technique, an event-based output feedback controller is developed together with a time-variant event-triggered rule. In this design, the radial basis function neural network algorithms are first introduced to identify the unknown terms of the systems. Then, a new state observer with adaptive compensation is designed to estimate the state vector...
September 18, 2018: IEEE Transactions on Cybernetics
Yipeng Ning, Jian Wang, Houzeng Han, Xinglong Tan, Tianjun Liu
Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors...
September 13, 2018: Sensors
Bhanu Pratap Soni, Akash Saxena, Vikas Gupta, S L Surana
Identification of transient stability state in real-time and maintaining stability through preventive control technology are challenging tasks for a large power system while integrating deregulation constraints. Widely employment of the phasor measurement units (PMUs) in a power system and development of wide area management systems (WAMS) give relaxation to monitoring, measurement and control hurdles. This paper focuses on two research objectives; the first is transient stability assessment (TSA) and second is selection of the appropriate member for the control operation in unstable operating scenario...
August 14, 2018: ISA Transactions
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