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Game-Theoretic Multi-Channel Multi-Access in Energy Harvesting Wireless Sensor Networks

IEEE SENSORS JOURNAL 2016年第11期16卷 4587-4594页

作者： Zheng, Jianchao Zhang, Honggang Cai, Yueming Li, Rongpeng Anpalagan, Alagan PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Huawei Technol Co Ltd Shanghai 210652 Peoples R China Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

Energy harvesting (EH) has been proposed as a promising technology to extend the lifetime of wireless sensor networks (WSNs) by continuously harvesting green/renewable energy. However, the intermittent and random EH process as well as the complexity in achieving global network information call for efficient energy management and distributed resource optimization. Considering the complex interactions among individual sensors, we use the game theory to perform distributed optimization for the general multi-channel multi-access problem in an EH-WSN, where strict delay constraints are imposed for the data transmission. Sensors' competition for channel access is formulated as a non-cooperative game, which is proved to be an ordinal potential game that has at least one Nash equilibrium (NE). Furthermore, all the NE of the game is proved to be Pareto optimal, and Jain's fairness index bound of the NE is theoretically derived. Finally, we design a fully distributed, online learning algorithm for the multi-channel multi-access in the EH-WSN, which is proved to converge to the NE of the formulated game. Simulation results validate the effectiveness of the proposed algorithm.

关键词： Wireless sensor network energy harvesting multi-channel multi-access potential game Nash equilibrium distributed online learning algorithm

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Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks

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PATTERN RECOGNITION LETTERS 2011年第14期32卷 1930-1935页

作者： Hieu Trung Huynh Won, Yonggwan HoChiMinh City Univ Ind Fac Informat Technol Ho Chi Minh City Vietnam Chonnam Natl Univ Dept Comp Engn Kwangju 500757 South Korea Chonnam Natl Univ Korea Bio IT Foundry Ctr Gwangju Kwangju 500757 South Korea Nguyen Tat Thanh Univ NTT Inst Hitechnol Ho Chi Minh City Vietnam

online learning algorithms have been preferred in many applications due to their ability to learn by the sequentially arriving data. One of the effective algorithms recently proposed for training single hidden-layer feedforward neural networks (SLFNs) is online sequential extreme learning machine (OS-ELM), which can learn data one-by-one or chunk-by-chunk at fixed or varying sizes. It is based on the ideas of extreme learning machine (ELM), in which the input weights and hidden layer biases are randomly chosen and then the output weights are determined by the pseudo-inverse operation. The learning speed of this algorithm is extremely high. However, it is not good to yield generalization models for noisy data and is difficult to initialize parameters in order to avoid singular and ill-posed problems. In this paper, we propose an improvement of OS-ELM based on the bi-objective optimization approach. It tries to minimize the empirical error and obtain small norm of network weight vector. Singular and ill-posed problems can be overcome by using the Tikhonov regularization. This approach is also able to learn data one-by-one or chunk-by-chunk. Experimental results show the better generalization performance of the proposed approach on benchmark datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Neural networks online learning algorithm ELM OS-ELM ReOS-ELM Multiobjective training algorithms

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Fuzzy supervised online coactive neuro-fuzzy inference system-based rotor position control of brushless DC motor

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IET POWER ELECTRONICS 2016年第11期9卷 2229-2239页

作者： Prabu, M. John Poongodi, P. Premkumar, K. Municipal Adm & Water Supply Dept Udumalpet Municipality Tamil Nadu India Karpagam Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India Rajalakshmi Engn Coll Dept Elect & Elect Engn Madras Tamil Nadu India

In this study, fuzzy supervised online coactive neuro-fuzzy inference system (CANFIS)-based rotor position controller is presented for brushless DC (BLDC) motor. An online learning algorithm is employed for updating premises and consequent parameters of the CANFIS controller. The rotor position control of BLDC motor is simulated using MATLAB/Simulink Toolbox. The dynamic response of the BLDC motor with proposed controller is measured for standard sinusoidal reference input. The effectiveness of the proposed controller performance is compared with fuzzy proportional-integral derivative controller, adaptive neuro-fuzzy inference system controller and supervised recurrent fuzzy neural network controller. The proposed controller is able to solve the problem of non-linearities and uncertainty due to reference input changes of BLDC motor and guarantees fast and accurate dynamic response to a remarkable steady-state performance. Also, experimental hardware results are presented to demonstrate the validity and effectiveness of the proposed control scheme using field programmable gate array chip. Experimental results show that the proposed control scheme can achieve a more favourable tracking performance without the chattering phenomena in the control effort.

关键词： fuzzy neural nets brushless DC motors machine control position control three-term control fuzzy control neurocontrollers control nonlinearities field programmable gate arrays recurrent neural nets adaptive control fuzzy supervised online coactive neuro-fuzzy inference system rotor position control brushless DC motor rotor position controller BLDC motor online learning algorithm CANFIS controller MATLAB Simulink Toolbox dynamic response fuzzy proportional-integral derivative controller adaptive neuro-fuzzy inference system controller supervised recurrent fuzzy neural network controller control nonlinearities field programmable gate array chip

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Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system

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IET RENEWABLE POWER GENERATION 2016年第7期10卷 988-1001页

作者： Lin, Faa-Jeng Tan, Kuang-Hsiung Tsai, Chia-Hung Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan Natl Def Univ Chung Cheng Inst Technol Dept Elect & Elect Engn Taoyuan 335 Taiwan

An improved differential evolution (IDE) algorithm-based Elman neural network (ENN) controller is proposed to control a squirrel-cage induction generator (SCIG) system for grid-connected wind power applications. First, the control characteristics of a wind turbine emulator are introduced. Then, an AC/DC converter and a DC/AC inverter are developed to convert the electric power generated by a three-phase SCIG to the grid. Moreover, the dynamic model of the SCIG system is derived for the control of the square of DC-link voltage according to the principle of power balance. Furthermore, in order to improve the transient and steady-state responses of the square of DC-link voltage of the SCIG system, an IDE-based ENN controller is proposed for the control of the SCIG system. In addition, the network structure and the online learning algorithm of the ENN are described in detail. Additionally, according to the different wind speed variations, a lookup table built offline by the dynamic model of the SCIG system using the IDE is provided for the optimisation of the learning rates of ENN. Finally, to verify the control performance, some experimental results are provided to verify the feasibility and the effectiveness of the proposed SCIG system for grid-connected wind power applications.

关键词： evolutionary computation neurocontrollers asynchronous generators machine control power grids wind power plants wind turbines power generation control invertors voltage control transient response learning (artificial intelligence) AC-DC power convertors improved differential evolution-based Elman neural network controller squirrel-cage induction generator system three-phase SCIG system grid-connected wind power applications wind turbine emulator control characteristics AC-DC power converter DC-AC power inverter electric power conversion DC-link voltage square control power balance principle steady-state responses transient responses IDE-based ENN intelligent controller algorithm network structure online learning algorithm wind speed variations lookup table learning rate optimization power grid

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A direct adaptive neural control for maximum power point tracking of photovoltaic system

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SOLAR ENERGY 2015年第May期115卷 145-165页

作者： Dounis, Anastasios I. Kofinas, P. Papadakis, G. Alafodimos, C. Technol Educ Inst Piraeus Dept Automat Egaleo 12244 Greece Agr Univ Athens Dept Nat Resources & Agr Engn Athens 11855 Greece

This paper represents a novel direct adaptive neural control (DANC) method for maximum power point tracking (MPPT) of photovoltaic (PV) systems. A DC/DC buck converter to regulate the output power of the photovoltaic system is considered. The direct adaptive neural control scheme operates on MPP and improves the performance of solar energy conversion efficiency. The online adaptation procedure is based on learning law of the delta rule and only the system output error is required. The prime contributions of this study are a simple and effective solution for MPPT, the fast learning and the straightforward digital implementation. The performance of direct adaptive neural controller on photovoltaic sources with different characteristics is assessed. The simulation results confirm the feasibility and effectiveness of the DANC. The proposed MPPT control system is compared to the conventional perturbation and observation (P&O) method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Direct adaptive neural control Maximum power point tracking Photovoltaic system online learning algorithm

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Quantised kernel least mean square with desired signal smoothing

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ELECTRONICS LETTERS 2015年第18期51卷 1457-1459页

作者： Xu, Xiguang Qu, Hua Zhao, Jihong Yang, Xiaohan Chen, Badong Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

The quantised kernel least mean square (QKLMS) is a simple yet efficient online learning algorithm, which reduces the computational cost significantly by quantising the input space to constrain the growth of network size. The QKLMS considers only the input space compression and assumes that the desired outputs of the quantised data are equal to those of the closest centres. In many cases, however, the outputs in a neighbourhood may have big differences, especially when the underlying system is disturbed by impulsive noises. Such fluctuation in desired outputs may seriously deteriorate the learning performance. To address this issue, a simple online method is proposed to smooth the desired signal within a neighbourhood corresponding to a quantisation region. The resulting algorithm is referred to as the QKLMS with desired signal smoothing. The desirable performance of the new algorithm is confirmed by Monte Carlo simulations.

关键词： smoothing methods least mean squares methods learning (artificial intelligence) Monte Carlo methods signal processing quantised kernel least mean square desired signal smoothing QKLMS online learning algorithm input space compression Monte Carlo simulations

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online learning algorithms for Train Automatic Stop Control Using Precise Location Data of Balises

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2013年第3期14卷 1526-1535页

作者： Chen, Dewang Chen, Rong Li, Yidong Tang, Tao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of +/- 0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.

关键词： Balise online learning algorithm reinforcement learning (RL) train automatic stop control (TASC) urban metro systems

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Incremental learning of context-dependent dynamic internal models for robot control

Incremental learning of context-dependent dynamic internal m...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： Jamone, Lorenzo Damas, Bruno Santos-Victor, Jose Univ Lisbon Inst Super Tecn Inst Sistemas & Robot P-1699 Lisbon Portugal Escola Super Tecnol Setubal Setubal Portugal

ISBN: (纸本)9781479974061

Accurate dynamic models can be very difficult to compute analytically for complex robots;moreover, using a pre-computed fixed model does not allow to cope with unexpected changes in the system. An interesting alternative solution is to learn such models from data, and keep them up-to-date through online adaptation. In this paper we consider the problem of learning the robot inverse dynamic model under dynamically varying contexts: the robot learns incrementally and autonomously the model under different conditions, represented by the manipulation of objects of different weights, that change the dynamics of the system. The inverse dynamic mapping is modeled as a multi-valued function, in which different outputs for the same input query are related to different dynamic contexts (i.e. different manipulated objects). The mapping is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for Computed Torque control. No information is given about the context switch during either learning or control, nor any assumption is made about the kind of variation in the dynamics imposed by a new contexts. Experimental results with the iCub humanoid robot are provided.

关键词： humanoid robots learning (artificial intelligence) manipulator dynamics regression analysis torque control IMLE computed torque control context-dependent dynamic internal models dynamically varying contexts iCub humanoid robot incremental learning input query inverse dynamic mapping multivalued function multivalued regression object manipulation online learning algorithm robot control robot inverse dynamic model Adaptation models Computational modeling Context Context modeling Joints Robots Training Humanoid robots learning (artificial intelligence) Robot control Torque control multivalued function Robot Context modeling manipulator dynamics Object Manipulation Computational modeling Joints Joints regression analysis Functional training Adaptation models

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Takagi-Sugeno-Kang type probabilistic fuzzy neural network control for grid-connected LiFePO₄ battery storage system

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IET POWER ELECTRONICS 2013年第6期6卷 1029-1040页

作者： Lin, Faa-Jeng Huang, Ming-Shi Hung, Ying-Chih Kuan, Chi-Hsuan Wang, Sheng-Long Lee, Yih-Der Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 106 Taiwan Inst Nucl Energy Res Engn Technol & Facil Operat Div Tao Yuan 335 Taiwan

A Takagi-Sugeno-Kang type probabilistic fuzzy neural network (TSKPFNN) control is proposed to control a grid-connected LiFePO4 battery storage system in this study. First, the modelling of the battery and bidirectional AC-DC converter are described in detail. Then, the active and reactive power controls using phase-lock loop are briefly introduced. Moreover, to improve the control performance of the grid-connected LiFePO4 battery storage system, the TSKPFNN control, which combines the advantages of Takagi-Sugeno-Kang type fuzzy logic system and three-dimensional membership function, is developed. The network structure, online learning algorithm using delta adaptation law and convergence analysis of the TSKPFNN are described in detail. Furthermore, a 32-bit fixed-point digital signal processor, TMS320F28035, is adopted for the implementation of the proposed intelligent controlled battery storage system. Finally, some experimental results are illustrated to show the validity of the proposed TSKPFNN control for the grid-connected LiFePO4 battery storage system.

关键词： battery storage plants digital signal processing chips fuzzy control fuzzy neural nets intelligent control iron compounds learning (artificial intelligence) lithium compounds neurocontrollers phase locked loops power generation control power grids probability reactive power control LiFePO4 word length 32 bit fixed-point digital signal processor TMS320F28035 convergence analysis delta adaptation law online learning algorithm three-dimensional membership function phase-lock loop active power control reactive power control bidirectional AC-DC converter battery modelling TSKPFNN control grid-connected battery storage system Takagi-Sugeno-Kang type probabilistic fuzzy neural network control

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Sparse online warped Gaussian process for wind power probabilistic forecasting

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APPLIED ENERGY 2013年 108卷 410-428页

作者： Kou, Peng Gao, Feng Guan, Xiaohong Xi An Jiao Tong Univ Syst Engn Inst SKLMS Xian 710049 Peoples R China Xi An Jiao Tong Univ Syst Engn Inst MOE KLINNS Xian 710049 Peoples R China Tsinghua Univ Ctr Intelligent & Networked Syst Beijing 100084 Peoples R China

Wind generation has experienced rapid growth around the world in the past decade. This highlights the importance of the short-term wind power forecasting. This paper focuses on the probabilistic short-term wind power forecasting. An online sparse Bayesian model is established. The key features of the proposed model are its non-Gaussian predictive distributions and its time-adaptiveness. This model based on the warped Gaussian process (WGP), which handles the non-Gaussian uncertainties in wind power series by automatically transforming it to a latent series. The transformed series is well-modeled by a Gaussian process (GP), then the non-Gaussian uncertainty associated with the wind power can be predicted in a standard GP framework. Wind generation is a process whose characteristics change with time, so a wind power forecasting model should exhibit adaptive features. To address this, we introduce an online learning algorithm to WGP, thus permitting WGP to track the time-varying characteristic of wind generation. Moreover, since the high computational costs of WGP hinder its practical application on large-scale problems such as wind power forecast, the proposed model also employs a sparsification method to reduce its computational costs, thus enhancing its practical applicability. The simulation on actual data validates the effectiveness of the proposed model. The data used in the simulation are obtained in the real operation of a wind farm in China. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Wind energy Probabilistic forecasting Gaussian process regression online learning algorithm Sparsification

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