The Experts below are selected from a list of 334596 Experts worldwide ranked by ideXlab platform
Derong Liu - One of the best experts on this subject based on the ideXlab platform.
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ISNN - Discrete-Time Two-Player Zero-Sum Games for Nonlinear Systems Using Iterative Adaptive Dynamic Programming
Advances in Neural Networks – ISNN 2016, 2016Co-Authors: Qinglai Wei, Derong LiuAbstract:This paper is concerned with a discrete-time two-player zero-sum game of nonlinear systems, which is solved by a new iterative adaptive dynamic programming (ADP) method. In the present iterative ADP algorithm, two iteration procedures, which are upper and lower iterations, are implemented to obtain the upper and lower performance Index Functions, respectively. Initialized by an arbitrary positive semi-definite Function, it is shown that the iterative value Functions converge to the optimal performance Index Function if the optimal performance Index Function of the two-player zero-sum game exists. Finally, simulation results are given to illustrate the performance of the developed method.
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Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors
Neurocomputing, 2015Co-Authors: Qinglai Wei, Derong LiuAbstract:In this paper, a new infinite horizon neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems is developed. The idea is to use iterative adaptive dynamic programming (ADP) algorithm to obtain the iterative tracking control law which makes the iterative performance Index Function reach the optimum. When the iterative tracking control law and iterative performance Index Function in each iteration cannot be accurately obtained, the convergence criteria of the iterative ADP algorithm are established according to the properties with finite approximation errors. If the convergence conditions are satisfied, it shows that the iterative performance Index Functions can converge to a finite neighborhood of the lowest bound of all performance Index Functions. Properties of the finite approximation errors for the iterative ADP algorithm are also analyzed. Neural networks are used to approximate the performance Index Function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Convergence properties of the neural network weights are proven. Finally, simulation results are given to illustrate the performance of the developed method. (C) 2014 Elsevier B.V. All rights reserved.
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data driven neuro optimal temperature control of water gas shift reaction using stable iterative adaptive dynamic programming
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Qinglai Wei, Derong LiuAbstract:In this paper, a novel data-driven stable iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal temperature control problems for water–gas shift (WGS) reaction systems. According to the system data, neural networks (NNs) are used to construct the dynamics of the WGS system and solve the reference control, respectively, where the mathematical model of the WGS system is unnecessary. Considering the reconstruction errors of NNs and the disturbances of the system and control input, a new stable iterative ADP algorithm is developed to obtain the optimal control law. The convergence property is developed to guarantee that the iterative performance Index Function converges to a finite neighborhood of the optimal performance Index Function. The stability property is developed to guarantee that each of the iterative control laws can make the tracking error uniformly ultimately bounded (UUB). NNs are developed to implement the stable iterative ADP algorithm. Finally, numerical results are given to illustrate the effectiveness of the developed method.
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A Novel Iterative theta-Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems
IEEE Transactions on Automation Science and Engineering, 2014Co-Authors: Qinglai Wei, Derong LiuAbstract:This paper is concerned with a new iterative theta-adaptive dynamic programming (ADP) technique to solve optimal control problems of infinite horizon discrete-time nonlinear systems. The idea is to use an iterative ADP algorithm to obtain the iterative control law which optimizes the iterative performance Index Function. In the present iterative theta-ADP algorithm, the condition of initial admissible control in policy iteration algorithm is avoided. It is proved that all the iterative controls obtained in the iterative theta-ADP algorithm can stabilize the nonlinear system which means that the iterative theta-ADP algorithm is feasible for implementations both online and offline. Convergence analysis of the performance Index Function is presented to guarantee that the iterative performance Index Function will converge to the optimum monotonically. Neural networks are used to approximate the performance Index Function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative theta-ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the established method.
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Nearly optimal control scheme for discrete-time nonlinear systems with finite approximation errors using generalized value iteration algorithm
IFAC Proceedings Volumes, 2014Co-Authors: Qinglai Wei, Derong LiuAbstract:Abstract In this paper, a new generalized value iteration algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The idea is to use iterative adaptive dynamic programming (ADP) to obtain the iterative control law which makes the iterative performance Index Function reach the optimum. The generalized value iteration algorithm permits an arbitrary positive semi-definite Function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance Index Function in each iteration cannot be accurately obtained, a new design method of the convergence criterion for the generalized value iteration algorithm with finite approximation errors is established to make the iterative performance Index Functions converge to a finite neighborhood of the lowest bound of all performance Index Functions. Simulation results are given to illustrate the performance of the developed algorithm.
Qinglai Wei - One of the best experts on this subject based on the ideXlab platform.
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An Iterative ADP Method to Solve for a Class of Nonlinear Zero-Sum Differential Games
Studies in Systems Decision and Control, 2018Co-Authors: Ruizhuo Song, Qinglai WeiAbstract:In this chapter, an iterative ADP method is presented to solve a class of continuous-time nonlinear two-person zero-sum differential games. The idea is to use ADP technique to obtain the optimal control pair iteratively which makes the performance Index Function reach the saddle point of the zero-sum differential games. When the saddle point does not exist, the mixed optimal control pair is obtained to make the performance Index Function reach the mixed optimum. Rigid proofs are proposed to guarantee the control pair stabilize the nonlinear system. And the convergent property of the performance Index Function is also proved. Neural networks are used to approximate the performance Index Function, compute the optimal control policy and model the nonlinear system respectively for facilitating the implementation of the iterative ADP method. Two examples are given to demonstrate the validity of the proposed method.
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Off-Policy Neuro-Optimal Control for Unknown Complex-Valued Nonlinear Systems
Studies in Systems Decision and Control, 2018Co-Authors: Ruizhuo Song, Qinglai WeiAbstract:This chapter establishes an optimal control of unknown complex-valued system. Policy iteration (PI) is used to obtain the solution of the Hamilton–Jacobi–Bellman (HJB) equation. Off-policy learning allows the iterative performance Index and iterative control to be obtained by completely unknown dynamics. Critic and action networks are used to get the iterative control and iterative performance Index, which execute policy evaluation and policy improvement. Asymptotic stability of the closed-loop system and the convergence of the iterative performance Index Function are proven. By Lyapunov technique, the uniformly ultimately bounded (UUB) of the weight error is proven. Simulation study demonstrates the effectiveness of the proposed optimal control method.
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ISNN - Discrete-Time Two-Player Zero-Sum Games for Nonlinear Systems Using Iterative Adaptive Dynamic Programming
Advances in Neural Networks – ISNN 2016, 2016Co-Authors: Qinglai Wei, Derong LiuAbstract:This paper is concerned with a discrete-time two-player zero-sum game of nonlinear systems, which is solved by a new iterative adaptive dynamic programming (ADP) method. In the present iterative ADP algorithm, two iteration procedures, which are upper and lower iterations, are implemented to obtain the upper and lower performance Index Functions, respectively. Initialized by an arbitrary positive semi-definite Function, it is shown that the iterative value Functions converge to the optimal performance Index Function if the optimal performance Index Function of the two-player zero-sum game exists. Finally, simulation results are given to illustrate the performance of the developed method.
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Off-policy neuro-optimal control for unknown complex-valued nonlinear systems based on policy iteration
Neural Computing and Applications, 2016Co-Authors: Ruizhuo Song, Qinglai Wei, Wendong XiaoAbstract:This paper establishes an optimal control of unknown complex-valued system. Policy iteration is used to obtain the solution of the Hamilton---Jacobi---Bellman equation. Off-policy learning allows the iterative performance Index and iterative control to be obtained by completely unknown dynamics. Critic and action networks are used to get the iterative control and iterative performance Index, which execute policy evaluation and policy improvement. Asymptotic stability of the closed-loop system and the convergence of the iterative performance Index Function are proven. By Lyapunov technique, the uniformly ultimately bounded of the weight error is proven. Simulation study demonstrates the effectiveness of the proposed optimal control method.
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Nearly optimal tracking control for continuous time nonlinear systems using a policy iteration based HJB approach
2015 34th Chinese Control Conference (CCC), 2015Co-Authors: Ruizhuo Song, Qinglai Wei, Wendong XiaoAbstract:A policy iteration method is proposed to solve the optimal tracking control of continuous-time systems based on HJB equation. The performance Index Function is composed by the state tracking error and the tracking control error. The iterative performance Index Function and the iterative control are obtained by the presented policy iteration. It is proven that the iterative control makes the system asymptotic stability, and the iterative performance Index Function is convergent. Simulation study demonstrates that the effectiveness of the proposed optimal tracking control method.
Kuang Yu Huang - One of the best experts on this subject based on the ideXlab platform.
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Multi-attribute decision-making based on rough set theory and modified PBMF-Index Function
IEEE Conference Anthology, 2013Co-Authors: Ting-cheng Chang, Kuang Yu Huang, Chuen-jiuan JaneAbstract:A Function is proposed for descritizing and classifying the uncertain data of multi-attribute decision-making (MADM) datasets using a hybrid scheme incorporating fuzzy set theory, Rough Set (RS) theory and a modified form of the PBMF Index Function. The proposed MADM Index Function is used to extend the applicability of the single-attribute decision-making (SADM) Function. The validity of the proposed MADM Index Function is evaluated by comparing the descritizing results obtained for a simple hypothetical Function with those obtained using a SADM Function and the conventional PBMF Function.
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An enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-Index Function
Applied Soft Computing, 2012Co-Authors: Kuang Yu HuangAbstract:This study proposes a method, designated as the GRP-Index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The proposed method consists of a genetic algorithm (GA) and an FRP-Index method. In the FRP-Index method, the conditional and decision attribute values of the instances in the dataset are fuzzified and discretized using the Fuzzy C-means (FCM) method in accordance with the cluster vectors given by the GA specifying the number of clusters per attribute. Rough set (RS) theory is then applied to determine the lower and upper approximate sets associated with each cluster of the decision attribute. The accuracy of approximation of each cluster of the decision attribute is then computed as the cardinality ratio of the lower approximate sets to the upper approximate sets. Finally, the centroids of the lower approximate sets associated with each cluster of the decision attribute are determined by computing the mean conditional and decision attribute values of all the instances within the corresponding sets. The cluster centroids and accuracy of approximation are then processed by a modified form of the PBMF-Index Function, designated as the RP-Index Function, in order to determine the optimality of the discretization/classification results. In the event that the termination criteria are not satisfied, the GA modifies the initial population of cluster vectors and the FCM, RS and RP-Index Function procedures are repeated. The entire process is repeated iteratively until the termination criteria are satisfied. The maximum value of the RP cluster validity Index is then identified, and the corresponding cluster vector is taken as the optimal classification result. The validity of the proposed approach is confirmed by cross validation, and by comparing the classification results obtained for a typical stock market dataset with those obtained by non-supervised and pseudo-supervised classification methods. The results show that the proposed GRP-Index method not only has a better discretization performance than the considered methods, but also achieves a better accuracy of approximation, and therefore provides a more reliable basis for the extraction of decision-making rules.
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Application of enhanced cluster validity Index Function to automatic stock portfolio selection system
Information Technology and Management, 2011Co-Authors: Kuang Yu Huang, Shiuan WanAbstract:This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF Index method (designated as the Huang Index Function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.
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A hybrid approach to continuous valued datasets classifying based on particle swarm optimization, variable precision rough set theory and modified huang-Index Function
WSEAS Transactions on Information Science and Applications archive, 2011Co-Authors: Kuang Yu HuangAbstract:This paper proposed a new hybrid method, designated as PSOVPRS-Index method, for partitioning and classifying continuous valued datasets based on particle swarm optimization (PSO) algorithm, Variable Precision Rough Set (VPRS) theory and a modified form of the Huang-Index Function. In contrast to the Huang-based Index method which simply assigns a constant number of clusters to each attribute and in which the Rough Set (RS) theory is applied, this method could not only cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy, but also extends the applicability of classification using VPRS theory. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset containing stock market information with those obtained by PSORS-Index method and pseudo-supervised decision-tree classification method. There is good evidence to show that the proposed PSOVPRS-Index method not only has a better classification performance than the considered methods, but also achieves a more reliable basis for the extraction of decision-making rules.
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FUZZ-IEEE - Integrate Variable Precision Rough Sets and modified PBMF Index Function for partitioning and classifying complex datasets
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Kuang Yu Huang, Yu-hsin ChengAbstract:This study proposes a method for partitioning and classifying complex datasets using a hybrid method based on Fuzzy C-Means (FCM) method, Variable Precision Rough Set (VPRS) theory and a modified form of the PBMF Index Function (a cluster validity Index Function). The proposed VPRS Index method partitions the attributes within the dataset rather than the data and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is confirmed by comparing the clustering results obtained from the VPRS method for a hypothetical Function and a typical stock market system with those obtained from the conventional RS and PBMF methods, respectively. Overall, the results show that the VPRS Index method not only has a better clustering performance than the PBMF method, but also achieves greater classification accuracy, and therefore provides a more reliable basis for the extraction of decision-making rules.
Ashwin Nayak - One of the best experts on this subject based on the ideXlab platform.
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The space complexity of recognizing well-parenthesized expressions in the streaming model: the Index Function revisited
IEEE Transactions on Information Theory, 2014Co-Authors: Rahul Jain, Ashwin NayakAbstract:We show an ( p n=T) lower bound for the space required by any unidirectional constanterror randomized T-pass streaming algorithm that recognizes whether an expression over two types of parenthesis is well-parenthesized. This proves a conjecture due to Magniez, Mathieu, and Nayak (2009) and rigorously establishes the peculiar power of bi-directional streams over unidirectional ones observed in the algorithms they present. The lower bound is obtained by analysing the information that is necessarily revealed by the players about their respective inputs in a two-party communication protocol for a variant of the Index Function.
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The space complexity of recognizing well-parenthesized expressions in the streaming model: the Index Function revisited
arXiv: Computational Complexity, 2010Co-Authors: Rahul Jain, Ashwin NayakAbstract:We show an Omega(sqrt{n}/T) lower bound for the space required by any unidirectional constant-error randomized T-pass streaming algorithm that recognizes whether an expression over two types of parenthesis is well-parenthesized. This proves a conjecture due to Magniez, Mathieu, and Nayak (2009) and rigorously establishes that bidirectional streams are exponentially more efficient in space usage as compared with unidirectional ones. We obtain the lower bound by establishing the minimum amount of information that is necessarily revealed by the players about their respective inputs in a two-party communication protocol for a variant of the Index Function, namely Augmented Index. The information cost trade-off is obtained by a novel application of the conceptually simple and familiar ideas such as average encoding and the cut-and-paste property of randomized protocols. Motivated by recent examples of exponential savings in space by streaming quantum algorithms, we also study quantum protocols for Augmented Index. Defining an appropriate notion of information cost for quantum protocols involves a delicate balancing act between its applicability and the ease with which we can analyze it. We define a notion of quantum information cost which reflects some of the non-intuitive properties of quantum information and give a trade-off for this notion. While this trade-off demonstrates the strength of our proof techniques, it does not lead to a space lower bound for checking parentheses. We leave such an implication for quantum streaming algorithms as an intriguing open question.
Yu-hsin Cheng - One of the best experts on this subject based on the ideXlab platform.
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FUZZ-IEEE - Integrate Variable Precision Rough Sets and modified PBMF Index Function for partitioning and classifying complex datasets
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Kuang Yu Huang, Yu-hsin ChengAbstract:This study proposes a method for partitioning and classifying complex datasets using a hybrid method based on Fuzzy C-Means (FCM) method, Variable Precision Rough Set (VPRS) theory and a modified form of the PBMF Index Function (a cluster validity Index Function). The proposed VPRS Index method partitions the attributes within the dataset rather than the data and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is confirmed by comparing the clustering results obtained from the VPRS method for a hypothetical Function and a typical stock market system with those obtained from the conventional RS and PBMF methods, respectively. Overall, the results show that the VPRS Index method not only has a better clustering performance than the PBMF method, but also achieves greater classification accuracy, and therefore provides a more reliable basis for the extraction of decision-making rules.
