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Active learning for regression based on query by committee

引用

8th international conference on intelligent data engineering and automated learning

作者： Bujrbidge, Robert Rowland, Jefn J. King, Ross D. Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

ISBN: (纸本)9783540772255

We investigate a committee-based approach for active learning of real-valued functions. This is a variance-only strategy for selection of informative training data. As such it is shown to suffer when the model class is misspecified since the learner's bias is high. Conversely, the strategy outperforms passive selection when the model class is very expressive since active minimization of the variance avoids overfitting.

关键词： Query processing

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A new arimoto-type algorithm to estimate states for repetitive processes: Iterative learning observer (ILO)

A new arimoto-type algorithm to estimate states for repetiti...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Moore, Kevin L. Hätönen, Jari IEEE Borealis Polymers Oy P.O. Box 330 Fi-06101 Finland Control Engineering Laboratory Helsinki University of Technology PL 5500 FI-02015 TKK Finland Department of Engineering Division of Engineering Colorado School of Mines Golden CO 80401 United States

ISBN: (纸本)142440441X

Iterative learning control (ILC) has been established as a very powerful technique to achieve high performance control for repetitive processes. In addition to the ILC problem, in the literature researchers have considered related paradigms such as iterative feedback tuning and iterative parameter estimation and identification. In the paper we introduce another related problem: the iterative learning observer. This dual problem establishes algorithms that can achieve high performance estimation of states for iterative processes. In order to solve the dual problem, this paper develops an estimation algorithm that estimates states asymptotically along the iteration axis. The theoretical findings are illustrated through simulation examples. © 2007 IEEE.

关键词： State estimation

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Optimal tuning of PID parameters using iterative learning approach

Optimal tuning of PID parameters using iterative learning ap...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Xu, Jian-Xin Huang, Deqing Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore

ISBN: (纸本)142440441X

PID is the most predominant industrial controller that constitutes more than 90% feedback loops. Time domain performance of PID, including overshoot, settling time and rise time, is directly relevant to the tuning of PID parameters. In this work we propose an optimal tuning method for PID by means of iterative learning. PID parameters will be updated whenever the same control task is repeated. A novel property of the new tuning method is that the time domain performance can be incorporated directly into the objective function to be minimized. Another novel property is that the optimal tuning does not require as much the process model knowledge as other PID tuning methods. The new tuning method is essentially applicable to any processes that are stabilizable by the PID controller. © 2007 IEEE.

关键词： Proportional control systems

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A unit-gain D-type iterative learning control scheme: Application to a 6-DOF robot manipulator

A unit-gain D-type iterative learning control scheme: Applic...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Ye, Yongqiang Tayebi, Abdelhamid Liu, Xiaoping Department of Electrical Engineering Lakehead University Thunder Bay ON P7B 5E1 Canada

ISBN: (纸本)142440441X

In this paper, a novel method to realize a unit-gain feature in iterative learning control (ILC) is proposed, using both forward and backward filtering. Based on this method, a unit-gain derivative is proposed as a remedy to the undesirable high gain feature of the conventional derivative at high frequency. The scheme is equivalent to an all-pass unit-gain phase shifter;the forward filtering uses a 0.5-order derivative and the backward filtering employs a 0.5-order integral. The all-pass phase shifter is then deployed in a unit-gain D-type ILC. For implementation, frequency-band synthesis of non-integer differentiator is introduced. The effectiveness of the unit-gain D-type ILC is demonstrated by experimental results on a 6 DOF robot manipulator. © 2007 IEEE.

关键词： Gain control

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Near optimal neural network-based output feedback control of affine nonlinear discrete-time systems

Near optimal neural network-based output feedback control of...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Qinmin, Yang Jagannathan, S. Department of Electrical and Computer Engineering University of Missouri Rolla MO 65401 United States

ISBN: (纸本)142440441X

In this paper, a novel online reinforcement learning neural network (NN)-based optimal output feedback controller, referred to as adaptive critic controller, is proposed for affine nonlinear discrete-time systems, to deliver a desired tracking performance. The adaptive critic design consist of three entities, an observer to estimate the system states, an action network that produces optimal control input and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function which is based on the standard Bellman equation. By using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the estimation and tracking errors and weight estimates is demonstrated. The effectiveness of the controller is evaluated for the task of nanomanipulation in a simulation environment. ©2007 IEEE.

关键词： Discrete time control systems

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Stability and robustness analysis of cyclic pseudo-downsampled ILC

Stability and robustness analysis of cyclic pseudo-downsampl...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Zhang, Bin Wang, Danwei Ye, Yongqiang Zhou, Keliang Wang, Yigang School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore School of Electrical and Electronic Engineering Nangyang Technological University Singapore Singapore School of Automation Hangzhou Dianzi University China Department of Electrical Engineering Southeast University Nanjing China

ISBN: (纸本)142440441X

In this paper, a multirate cyclic pseudo-downsampled iterative learning control (ILC) scheme is proposed. The scheme has the ability to produce good learning transient for trajectories with high frequency components and/or initial state errors. The proposed scheme downsamples the feedback error and input signals every m samples to arrive at slow rate signals. Then, the downsampled slow rate signals are applied to ILC algorithm, whose output is then interpolated and applied to actuator. The novelty of the proposed scheme is that, for two successive iterations, the signal is downsampled with the same m but the downsampling points are time shifted along the time axis. This shifting process makes the ILC scheme cyclic along the iteration axis with a period of m cycles. Stability and robustness analysis shows that good learning transient can be guaranteed. Simulation results show significant tracking accuracy improvement. Additional advantages are that the proposed scheme does not need a filter design and reduces the computation load and memory size substantially. The proposed scheme can be applied to the control of other rotatory machinery. © 2007 IEEE.

关键词： learning systems

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An efficient solution for compositional design problems by Multi-stage Genetic Algorithm

An efficient solution for compositional design problems by M...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Suzuki, Masakazu Hiyama, Yuki Yamada, Hideki School of Engineering Tokai University 1117 Kitakaname Hiratsuka 259-1292 Japan

ISBN: (纸本)142440441X

The Multi-stage Genetic Algorithm, MGA, is introduced to solve a class of compositional design problems. The problem with complicated constraints is formulated as a set of local subproblems with simple constraints and a supervising problem. Every subproblem is solved by GA to generate a set of suboptimal solutions. And in the supervising problem, the elements of each set are optimally combined by GA to yield the optimal solution for the original problem. The method is a learning method where the empirical knowledge obtained by solving the problem is effectively utilized to solve similar problems efficiently. Extended knapsack problems are solved to demonstrate the proposed method, and the efficiency of the method is shown. In addition, the method is successfully applied to optimal realization of cooperative robot soccer behaviors. © 2007 IEEE.

关键词： Problem solving

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Weighted fuzzy rule-based system combined with a novel simplified *** foraging optimization algorithm

Weighted fuzzy rule-based system combined with a novel simpl...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Shizhuang, Lin Yijian, Liu Yanjun, Fang Department of Automation Wuhan University Wuhan 430027 China College of Electric and Automation Engineering Nanjing Normal University Nanjing 210042 China Shajao C Power Station of Guangdong YuDean Group Co. Ltd. China

ISBN: (纸本)142440441X

A simplified *** foraging optimization algorithm is presented in this paper, which simulates the chemotactic behavior of ***. The optimization algorithm characterizes the easy implementation and the fact that no gradient information is required. The operators of the algorithm are described in details. The simplified *** algorithm consists of a tumbling operator and a swimming operator. At the same time the optimal position of individual *** and the location of all *** swarm are adopted to update the locations of swarm. In this paper, a weighted fuzzy rule-based system has been designed, in which the parameters of membership functions including position and shape of the fuzzy rule set and weights of rules are estimated using the simplified *** foraging optimization algorithm. The efficiency of the system has been illustrated in the process of classifying the iris data. This paper also shows that weighted fuzzy rules can lead to better fuzzy system compared with non-weighted fuzzy rules. © 2007 IEEE.

关键词： Escherichia coli

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Reinforcement learning based output-feedback controller for complex nonlinear discrete-time systems

Reinforcement learning based output-feedback controller for ...

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Shih, Peter Jagannathan, S. Department of Electrical and Computer Engineering University of Missouri-Rolla

ISBN: (纸本)142440441X

A novel reinforcement-learning based outputadaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex feedback nonlinear discrete-time systems in the presence of bounded and unknown disturbances. This nonlinear discrete-time system consists of a second order system in nonstrict form and an affine nonlinear discrete-time system tightly coupled together. Two adaptive critic NN controllers are designed-primary one for the nonstrict system and the secondary one for the affine system. A Lyapunov function shows the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates and observer estimates. Separation principle and certainty equivalence principles are relaxed, persistency of excitation condition is not required and linear in the unknown parameter assumption is not needed. The performance of this controller is evaluated on a spark ignition (SI) engine operating with high exhaust gas recirculation (EGR) levels where the objective is to reduce cyclic dispersion in heat release. © 2007 IEEE.

关键词： Feedback control

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A general internal model approach for motion learning

A general internal model approach for motion learning

引用

2007 IEEE 22nd international Symposium on intelligent Control, ISIC 2007

作者： Xu, Jian-Xin Wang, Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore

ISBN: (纸本)142440441X

In this article, we present a general internal model (GIM) approach for motion skill learning at the elementary level and coordination level. In the past, internal models with two different configurations are used to describe the two classes of Dynamic Movement Primitives (DMPs): discrete and rhythmic movement. In this work, we developed a unified internal model which can describe both classes of DMPs. In particular, a discrete movement can be modeled as a fraction of a rhythmic movement. The general internal model retains the temporal and spatial scalabilities which are defined as the ability to generate similar movement patterns directly by means of tuning some parameters of the internal model. The advantage of scalability lies in that the learning or training process can be avoided while dealing with similar tasks. Complex motions require movement coordinations, hence coordination of multiple internal models. In the general internal model approach, the coordination is implemented with appropriate phase shifts among multiple internal models. Further in the GIM, the phase shift can be achieved by means of adjusting the initial state values of internal models. Through two illustrative examples, we show that the human behavior patterns with single or multiple limbs can be easily learned and established by the GIM at elementary and coordination levels. © 2007 IEEE.

关键词： Motion control

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