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radial basis function neural network

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
Qing Guo, Yi Zhang, Branko G Celler, Steven W Su
This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration...
August 30, 2018: IEEE Transactions on Neural Networks and Learning Systems
Gong Cheng, Liming Sun, Jie Fu
Polycyclic aromatic hydrocarbons (PAHs) have attracted great concern as global environmental pollutants. In this work, the quantitative structure-biodegradability relationship (QSBR) study has been done to predict the biodegradability of PAHs and develop the correlation between the biodegradability and the molecular structures. The structural chemistry and quantum chemistry descriptors were used to represent molecular structures. Three in silico modeling methods, i.e., multiple linear regression (MLR), radial basis function neural network, and back-propagation artificial neural network (BPANN), are utilized to construct the linear and nonlinear prediction models and provide some insights into the structural characteristics affecting the biodegradability of PAHs...
November 2018: Archives of Environmental Contamination and Toxicology
Yexing Wang, Humin Lei, Jikun Ye, Xiangwei Bu
This paper investigates the design of a missile seeker servo system combined with a guidance and control system. Firstly, a complete model containing a missile seeker servo system, missile guidance system, and missile control system (SGCS) was creatively proposed. Secondly, a designed high-order tracking differentiator (HTD) was used to estimate states of systems in real time, which guarantees the feasibility of the designed algorithm. To guarantee tracking precision and robustness, backstepping sliding-mode control was adopted...
September 3, 2018: Sensors
Ben Niu, Ding Wang, Naif D Alotaibi, Fuad E Alsaadi
In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem...
August 20, 2018: IEEE Transactions on Neural Networks and Learning Systems
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
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
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...
September 2018: Journal de Mycologie Médicale
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
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
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
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
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
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
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
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