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radial basis function neural network

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https://www.readbyqxmd.com/read/30093857/movement-based-control-for-upper-limb-prosthetics-is-the-regression-technique-the-key-to-a-robust-and-accurate-control
#1
Mathilde Legrand, Manelle Merad, Etienne de Montalivet, Agnès Roby-Brami, Nathanaël Jarrassé
Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements...
2018: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/30067705/radial-basis-function-neural-network-enabled-c-band-4-%C3%A3-50-gb-s-pam-4-transmission-over-80-km-ssmf
#2
Zheng Yang, Fan Gao, Songnian Fu, Xiang Li, Lei Deng, Zhixue He, Ming Tang, Deming Liu
We propose a novel radial basis function neural network (RBF-NN)-based nonlinear equalizer (NLE) for the intensity modulation/direct detection (IM/DD) transmission. After optimizing input characteristics of the RBF-NN, we experimentally demonstrate C-band 4×50  Gb/s four-level pulse-amplitude modulation (PAM-4) transmission over 80 km standard single-mode fiber (SSMF), using 18 GHz direct-modulated lasers and dispersion compensation fiber. We demonstrate that the RBF-NN-based NLE outperforms the commonly used Volterra filter equalizer and recently proposed multilayer perceptron (MLP)-based NLE by about 4...
August 1, 2018: Optics Letters
https://www.readbyqxmd.com/read/30057154/statistical-versus-artificial-intelligence-based-modeling-for-the-optimization-of-antifungal-activity-against-fusarium-oxysporum-using-streptomyces-sp-strain-tn71
#3
S Smaoui, K Ennouri, A Chakchouk-Mtibaa, I Sellem, K Bouchaala, I Karray-Rebai, L Mellouli
A Streptomyces sp. strain TN71 was isolated from Tunisian Saharan soil and selected for its antimicrobial activity against phytopathogenic fungi. In an attempt to increase its anti-Fusarium oxysporum activity, GYM+S (glucose, yeast extract, malt extract and starch) culture medium was selected out of five different production media. Plackett-Burman design (PBD) was used to select yeast extract, malt extract and calcium carbonate (CaCO3 ) as parameters having significant effects on antifungal activity, and a Box-Behnken design was applied for further optimization...
July 26, 2018: Journal de Mycologie Médicale
https://www.readbyqxmd.com/read/30047914/robot-learning-system-based-on-adaptive-neural-control-and-dynamic-movement-primitives
#4
Chenguang Yang, Chuize Chen, Wei He, Rongxin Cui, Zhijun Li
This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During robot learning demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set of dynamic systems that enhances the stability of the generated motion toward the goal. A Gaussian mixture model and Gaussian mixture regression are integrated to improve the learning performance of the DMP, such that more features of the skill can be extracted from multiple demonstrations...
July 26, 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/30041862/anti-disturbance-dynamic-surface-control-scheme-for-a-class-of-uncertain-nonlinear-systems-with-asymmetric-dead-zone-nonlinearity
#5
Maryam Shahriari-Kahkeshi
This study proposes anti-disturbance dynamic surface control scheme for nonlinear strict-feedback systems subjected simultaneously to unknown asymmetric dead-zone nonlinearity, unmatched external disturbance and uncertain nonlinear dynamics. Radial basis function-neural network (RBF-NN) is invoked to approximate the uncertain dynamics of the system, and the dead-zone nonlinearity is represented as a time-varying system with a bounded disturbance. The nonlinear disturbance observer (NDO) is proposed to estimate the unmatched external disturbance which further will be used to compensate the effect of the disturbance...
July 2, 2018: ISA Transactions
https://www.readbyqxmd.com/read/30001401/brain-haemorrhage-detection-using-a-svm-classifier-with-electrical-impedance-tomography-measurement-frames
#6
Barry McDermott, Martin O'Halloran, Emily Porter, Adam Santorelli
Brain haemorrhages often require urgent treatment with a consequent need for quick and accurate diagnosis. Therefore, in this study, we investigate Support Vector Machine (SVM) classifiers for detecting brain haemorrhages using Electrical Impedance Tomography (EIT) measurement frames. A 2-layer model of the head, along with a series of haemorrhages, is designed as both numerical models and physical phantoms. EIT measurement frames, taken from an electrode array placed on the head surface, are used to train and test linear SVM classifiers...
2018: PloS One
https://www.readbyqxmd.com/read/29994516/adaptive-neural-dynamic-surface-control-for-nonstrict-feedback-systems-with-output-dead-zone
#7
Xiaocheng Shi, Cheng-Chew Lim, Peng Shi, Shengyuan Xu
This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality...
February 6, 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29994134/optimized-neural-network-parameters-using-stochastic-fractal-technique-to-compensate-kalman-filter-for-power-system-tracking-state-estimation
#8
Hossam Mosbah, Mohamed E El-Hawary
Tracking-state estimation uses previous state vector and recent measurement data to give real-time update on the state of the power system noniteratively during the subsequent time sampling. This paper discusses Kalman filtering enhanced by optimized neural network parameters-based stochastic fractals search technique (KF-MLP-based SFS). Both KF gain (mismodeling error) and measurement noise were replaced by optimized multilayer perceptron (MLP-SFS). This optimized MLP-based SFS could suppress filter divergence and improve the accuracy...
June 21, 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29994017/neural-networks-based-adaptive-finite-time-fault-tolerant-control-for-a-class-of-strict-feedback-switched-nonlinear-systems
#9
Lei Liu, Yan-Jun Liu, Shaocheng Tong
This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary...
May 4, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/29993904/neural-networks-enhanced-adaptive-admittance-control-of-optimized-robot-environment-interaction
#10
Chenguang Yang, Guangzhu Peng, Yanan Li, Rongxin Cui, Long Cheng, Zhijun Li
In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking...
May 8, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/29993704/global-and-local-surrogate-assisted-differential-evolution-for-expensive-constrained-optimization-problems-with-inequality-constraints
#11
Yong Wang, Da-Qing Yin, Shengxiang Yang, Guangyong Sun
For expensive constrained optimization problems (ECOPs), the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution (DE) for solving ECOPs with inequality constraints. The proposed method consists of two main phases: 1) global surrogate-assisted phase and 2) local surrogate-assisted phase. In the global surrogate-assisted phase, DE serves as the search engine to produce multiple trial vectors. Afterward, the generalized regression neural network is used to evaluate these trial vectors...
March 29, 2018: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/29993670/adaptive-neural-tracking-control-of-switched-stochastic-pure-feedback-nonlinear-systems-with-unknown-bouc-wen-hysteresis-input
#12
Zahra Namadchian, Modjtaba Rouhani
This paper aims to analyze the problem of adaptive neural network (NN) tracking control for a class of switched stochastic nonlinear pure-feedback systems with unknown direction hysteresis. In the light of recent studies on the hysteresis phenomenon in the field of nonlinear switched systems, this paper focuses on Bouc-Wen hysteresis model with unknown parameters and direction conditions. To simplify the control design, the following procedure is applied. Prior to tackling the unknown direction hysteresis problem based on the Nussbaum function and the backstepping techniques, the pure-feedback structure difficulty is governed by the mean value theorem...
April 5, 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29993614/recursive-neural-networks-for-density-estimation-over-generalized-random-graphs
#13
Marco Bongini, Leonardo Rigutini, Edmondo Trentin
Structured data in the form of labeled graphs (with variable order and topology) may be thought of as the outcomes of a random graph (RG) generating process characterized by an underlying probabilistic law. This paper formalizes the notions of generalized RG (GRG) and probability density function (pdf) for GRGs. Thence, a ``universal'' learning machine (combining the encoding module of a recursive neural network and a radial basis functions' network) is introduced for estimating the unknown pdf from an unsupervised sample of GRGs...
March 5, 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29990230/adaptive-neural-network-control-for-robotic-manipulators-with-unknown-deadzone
#14
Wei He, Bo Huang, Yiting Dong, Zhijun Li, Chun-Yi Su
This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control for robotic manipulators. State-feedback control is introduced first and a high-gain observer is then designed to make the proposed control scheme more practical. One radial basis function NN (RBFNN) is used to tackle the deadzone effect, and the other RBFNN is also proposed to estimate the unknown dynamics of robot...
December 11, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/29990131/neuroadaptive-control-with-given-performance-specifications-for-mimo-strict-feedback-systems-under-nonsmooth-actuation-and-output-constraints
#15
Yongduan Song, Shuyan Zhou
This paper studies the prescribed performance tracking control problem for a class of multi-input multi-output strict-feedback systems with asymmetric nonsmooth actuator characteristics and output constraints as well as unexpected external disturbances. By combining a novel speed transformation with barrier Lyapunov function, a neural adaptive control scheme is developed that is able to achieve given tracking precision within preassigned finite time at prespecified converging mode. At each of the first $n-1$ steps of backstepping design, we make use of the radial basis function neural networks to cope with the uncertainties arising from unknown and time-varying virtual control gains, and in the last step, we introduce a matrix factorization technique to remove the restrictive requirement on the unknown control gain matrix and its NN-approximation, simplifying control design...
November 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29990097/self-organizing-rbf-neural-network-using-an-adaptive-gradient-multiobjective-particle-swarm-optimization
#16
Honggui Han, Xiaolong Wu, Lu Zhang, Yu Tian, Junfei Qiao
One of the major obstacles in using radial basis function (RBF) neural networks is the convergence toward local minima instead of the global minima. For this reason, an adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm is designed to optimize both the structure and parameters of RBF neural networks in this paper. First, the AGMOPSO algorithm, based on a multiobjective gradient method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance...
October 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/29975883/exergetic-performance-prediction-of-solar-air-heater-using-mlp-grnn-and-rbf-models-of-artificial-neural-network-technique
#17
Harish Kumar Ghritlahre, Radha Krishna Prasad
In the present study three different types of neural models: multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis function (RBF) has been used to predict the exergetic efficiency of roughened solar air heater. The experiments were conducted at NIT Jamshedpur, India, using two different types of absorber plate: arc shape wire rib roughened with relative roughness height 0.0395, relative roughness pitch 10 and angle of attack 60°, and smooth absorber plates for 7 days. Total 210 data sets were collected from the experiments...
June 28, 2018: Journal of Environmental Management
https://www.readbyqxmd.com/read/29911250/evaluation-of-feature-extraction-techniques-and-classifiers-for-finger-movement-recognition-using-surface-electromyography-signal
#18
Pornchai Phukpattaranont, Sirinee Thongpanja, Khairul Anam, Adel Al-Jumaily, Chusak Limsakul
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated...
June 18, 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29900478/investigating-the-management-performance-of-disinfection-analysis-of-water-distribution-networks-using-data-mining-approaches
#19
Mohammad Zounemat-Kermani, Abdollah Ramezani-Charmahineh, Jan Adamowski, Ozgur Kisi
Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e...
June 13, 2018: Environmental Monitoring and Assessment
https://www.readbyqxmd.com/read/29899289/a-direct-position-determination-approach-for-multiple-sources-based-on-neural-network-computation
#20
Xin Chen, Ding Wang, Jiexin Yin, Ying Wu
The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands...
June 13, 2018: Sensors
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