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learning Optimal Scheduling Policy for Remote State Estimation Under Uncertain Channel Condition

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第2期7卷 579-591页

作者： Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Ctr Intelligent & Networked Syst Beijing 100084 Peoples R China KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden

We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming that the channel statistics is known. We prove that the Q-factor is monotonic and submodular, which leads to thresholdlike structures in both problems. Then, we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We, then prove the convergence of these algorithms. Performance improvement compared with the standard Q-learning algorithm is shown through numerical examples, which will also discuss an alternative method based on recursive estimation of the channel quality.

关键词： learning algorithm scheduling state estimation threshold structure

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Multiobjective Automated Type-2 Parsimonious learning Machine to Forecast Time-Varying Stock Indices Online

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2874-2887页

作者： Ferdaus, Md Meftahul Chakrabortty, Ripon K. Ryan, Michael J. ASTAR Dept Machine Intellect I2R Singapore Singapore Univ New South Wales Sch Engn & Informat Technol Australian Def Force Acad Canberra ACT 2612 Australia

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model's explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs' are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm's performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

关键词： Predictive models Prediction algorithms Data models Fuzzy logic Machine learning algorithms Fuzzy sets Forecasting Automated learning algorithm meta-heuristics multiobjective network complexity

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An Evolving Radial Basis Neural Network with Adaptive learning of Its Parameters and Architecture

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2015年第5期49卷 255-260页

作者： Bodyanskiy, Ye. V. Tyshchenko, A. K. Deineko, A. A. Kharkiv Natl Univ Radio Elect Pr Lenina 14 UA-61166 Kharkov Ukraine

The paper proposes a learning method for an evolving Radial Basis Neural Network that makes it possible in an online mode to adjust not only synaptic weights but also parameters of the radial basis functions and the network architecture. A special feature of architecture learning is that a number of neurons in the network can both increase and decrease with a sequential stream of information at the system input. The implementation of the proposed algorithms has low computational complexity. The proposed evolving neural network can process data in an online mode.

关键词： neuro-fuzzy network computational intelligence evolving system learning algorithm self-learning kernel function

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A learning-Based Approach for Agile Satellite Onboard Scheduling

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IEEE ACCESS 2020年 8卷 16941-16952页

作者： Lu, Ji Chen, Yuning He, Renjie Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

Autonomy increases the ability of earth observing satellites by allowing them to acquire more images. This is enabled by an efficient planning and scheduling algorithm which is able to make quick decisions onboard. Due to the NP-hardness of the agile earth observing satellite (AEOS) onboard scheduling problem, heuristic and metaheuristic algorithms seem to be appropriate to cope with increasingly enlarged problems. Also, the algorithms need to be intelligent enough to deal with dynamically changing situations onboard. Such algorithms are missing in the literature and we make the first attempt to propose a learning-based approach (LBA) for the AEOS onboard scheduling problem. LBA adopts an offline training - onboard scheduling paradigm where it trains a classifier using massive historical data offline on the ground and embeds this classifier to an onboard greedy construction algorithm. At each construction step, the greedy algorithm uses the classifier to test the potential of a task and arranges its observation time if it is accepted by the classifier. Extensive experimental results show that the proposed LBA is highly suitable for onboard use in terms of both solution quality and response time. In particular, LBA easily dominates state-of-the-art algorithms by producing very high quality solutions for large-size problems (with over 100 tasks) in seconds.

关键词： Agile autonomous satellite onboard scheduling learning algorithm feature selection

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Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第7期41卷 2039-2052页

作者： Kayacan, Erkan MIT Senseable City Lab Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

This paper presents a novel sliding mode control (SMC) algorithm to handle mismatched uncertainties in systems via a novel self-learning disturbance observer (SLDO). A computationally efficient SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a neuro-fuzzy structure (NFS) work in parallel. In this framework, the NFS estimates the mismatched disturbances and becomes the leading disturbance estimator while the former feeds the learning error to the NFS to learn system behaviour. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures robust control performance in the presence of mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC) and SMC based on a basic nonlinear disturbance observer (SMC-BNDO), and also remains the nominal control performance in the absence of mismatched uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched time-varying uncertainties, but also improve the transient response performance in the presence of mismatched time-invariant uncertainties. Moreover, the controller gain of the SMC-SLDO is required to be selected larger than the upper bound of the disturbance estimation error rather than the upper bound of the actual disturbance to guarantee system stability, which results in eliminating the chattering effects on the control signal.

关键词： disturbance observers learning algorithm mismatched uncertainty neuro-fuzzy structures sliding mode control theory

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learning-based constitutive parameters estimation in an image sensing system with multiple mirrors

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PATTERN RECOGNITION 2000年第7期33卷 1199-1217页

作者： Kim, WS Cho, HS Korea Adv Inst Sci & Technol Dept Mech Engn Taejon 305701 South Korea Samsung Elect Co Ltd Prod Engn Ctr FA Res Inst Suwon 441742 Kyungki Do South Korea

A sensing system sometimes requires a complicated optical unit consisting of multiple mirrors, in which case it is important to estimate accurately constitutive parameters of the optical unit to enhance its sensing capability. However, the parameters include generally uncertainties since the optical unit cannot avoid the fixing and aligning errors and the manufacturing tolerance of its components. Accordingly, it should construct a projective model of the complicated sensing system accurately and build up an estimation method of tangled parameters. However, it is not easy to estimate complicated constitutive parameters from an accurate model of an optical unit with multiple mirrors, and moreover, they are sometimes changed during operation due to unexpected disturbance or intermittent adjustments such as computer control zoom, auto focus, and mirror relocation. Due to these operational circumstances, it is not easy to take apart components of the assembled system and directly measure the components. Therefore, an indirect and adaptive estimation method, taking all the components into simultaneous consideration without disassembling the sensing system, is needed for calibrating the uncertain and changeable constitutive parameters. In this paper, we propose not only a generalized projective model for an optical sensing system consisting of n-mirrors and a camera with a collecting lens, bur also a learning-based process using the model to estimate recursively the uncertain constitutive parameters of the optical sensing system. We also show its feasibility through a series of calibration of an optical system. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： image sensing system multiple mirror learning algorithm estimation uncertainty

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PREDICTIVE FUZZY CONTROL OF AN AUTONOMOUS MOBILE ROBOT WITH FORECAST learning-FUNCTION

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FUZZY SETS AND SYSTEMS 1995年第1期72卷 51-60页

作者： MAEDA, M SHIMAKAWA, M MURAKAMI, S Department of Computer Engineering Faculty of Engineering Kyushu Institute of Technology Tobata Kitakyushu 804 Japan

This paper deals with the drive control of an autonomous mobile robot. An autonomous mobile robot is one of the intelligent robots that need abilities to recognize and to adapt to surrounding environment. We propose a new approach to meeting these needs. This approach is based on a forecast learning fuzzy control. The environment can be classified into several characteristic patterns and our robot has sets of control rules for each pattern beforehand. The robot integrates these sets into a single set using degrees of matching between the current environment and each pattern. The robot forecasts whether it will drive safely or not by prediction, by using the integrated control rules. The robot considers the results of the forecast, and then adjusts the conclusion parts of the integrated control rules in order to drive more safely in such an environment. In this paper, to find the efficacy of our new approach, the simulation results of the drive control of the robot and the experimental results on indoor routes are shown.

关键词： INTELLIGENT ROBOT FUZZY EXPERT SYSTEM FUZZY CONTROL PREDICTIVE CONTROL learning algorithm

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Iterative learning Control with Advanced Output Data for an Unknown Number of Non-minimum Phase Zeros

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2012年第8期E95A卷 1416-1419页

作者： Jeong, Gu-Min Moon, Chanwoo Ahn, Hyun-Sik Kookmin Univ Sch Elect Engn Seoul South Korea

This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.

关键词： iterative learning control non-minimum phase system learning algorithm advanced time

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Reinforcement learning control of nonlinear multi-link system

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2001年第5期14卷 563-575页

作者： Bucak, IO Zohdy, MA Oakland Univ Dept Elect & Syst Engn Rochester MI 48309 USA

In this paper, the effects of basic parameters in reinforcement learning control such as eligibility, action and critic network constrained weights, system nonlinearities, gradient information, state-space partitioning, variance of exploration are studied in detail. It is attempted to increase feasibility for practical applications, implementation, learning efficiency, and enhance performance. Also, a novel adaptive grid algorithm is proposed to overcome the difficulty in partitioning the input space to achieve better performance. Reinforcement learning is applied for control of a nonlinear one and two-link robots. This problem dictates that the learning is performed on-line, based on a binary or real-valued reinforcement signal from a critic network, without knowing the system model or nonlinearity. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： reinforcement learning learning control nonlinear control robotics learning algorithm

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Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge learning

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 3258-3271页

作者： Albaseer, Abdullatif Abdallah, Mohamed Al-Fuqaha, Ala Erbad, Aiman Hamad Bin Khlifa Univ Coll Sci & Engn Div Informat & Comp Technol Doha 5825 Qatar

In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leading to a decrease in their lifetime. This work proposes novel solutions for energy-efficient FEEL by jointly considering local training data, available computation, and communications resources, and deadline constraints of FEEL rounds to reduce energy consumption. This paper considers a system model where the edge server is equipped with multiple antennas employing beamforming techniques to communicate with the local users through orthogonal channels. Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL. Then, we devise tractable solutions by first proposing a novel fine-grained training algorithm that excludes less relevant training samples and effectively chooses only the samples that improve the model's performance. After that, we derive closed-form solutions, followed by a Golden-Section-based iterative algorithm to find the optimal computation and communication resources that minimize energy consumption. Experiments using MNIST and CIFAR-10 datasets demonstrate that our proposed algorithms considerably outperform the state-of-the-art solutions as energy consumption decreases by 79% for MNIST and 73% for CIFAR-10 datasets.

关键词： Servers Energy consumption Training Data models Computational modeling Resource management Performance evaluation Federated Edge learning (FEEL) Edge Intelligence Data selection learning algorithm Energy consumption Convergence rate Resource allocation

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