Efficient Approach for RLS Type Learning in TSK Neural Fuzzy Systems

Yeh, Jen-Wei; Su, Shun‐Feng

doi:10.1109/tcyb.2016.2638861

Cited by 28 publications

(14 citation statements)

References 46 publications

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“…We note that other supervised-learning methods, e.g. multi-relational classification models [78], deep fuzzy models [50,77], and deep residual models [27,45] may improve the prediction accuracy of the reward oracles and affect the performance of the algorithm. However, these implementations are expected to be explored further in the future works.…”

Section: B Experiments Settingsmentioning

confidence: 99%

Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments

Nguyen

2021

IEEE Access

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Learning action policy for autonomous agents in a decentralized multi-agent environment has remained an interesting but difficult research problem. We propose to model this problem in a contextual bandit setting with delayed reward signals, in particular an individual short-term reward signal and a shared long-term reward signal. Our algorithm utilizes an approach with reward oracles to directly model these delayed reward signals and also relies on a learning scheme benefiting from the sampling guidance of an expert-designed policy. This algorithm is expected to apply to a wide range of problems, including those with constraints on accessing state transitions and those with implicit reward information. A demonstration, deployed by deep learning regressors, shows the effectiveness of the proposed algorithm in learning offensive action policy in the RoboCup Soccer 2D Simulation (RCSS) environment against a well-known adversary benchmark team compared to a baseline policy.

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Section: B Experiments Settingsmentioning

confidence: 99%

Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments

Nguyen

2021

IEEE Access

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“…NFINs can be divided into two types: Mamdani-type NFINs (M-NFINs) (4,5) and Takagi-Sugeno-Kang (TSK)-type NFINs (T-NFINs). (6)(7)(8) The fuzzy reasoning in an M-NFIN involves the minimum fuzzy implication rule. However, in a T-NFIN, the consequence of each fuzzy rule is a linear function including a combination of input variables.…”

Section: Introductionmentioning

confidence: 99%

“…However, in a T-NFIN, the consequence of each fuzzy rule is a linear function including a combination of input variables. Many studies (7,8) have shown that a T-NFIN achieves superior performance to an M-NFIN in terms of accuracy and network size. In general, the function involving a combination of input variables in a T-NFIN is linear.…”

Section: Introductionmentioning

confidence: 99%

Neural Fuzzy Inference Network Based on Differential Evolution for Nonlinear System Control

Lin¹,

Lin²

2022

Sensors and Materials

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We propose a neural fuzzy inference network (NFIN) based on a symbiotic Taguchi-based modified differential evolution (STMDE) algorithm for solving nonlinear control problems. The proposed STMDE algorithm not only uses the Taguchi method in its search for the best individual but also employs adjustable parameter control to tune the scaling factor, which can prevent a solution from being trapped at local optima and reinforce the search ability. Moreover, symbiotic evolution (SE) is applied to improve the structure of individual compositions. Unlike the traditional differential evolution (DE) algorithm, SE regards each individual in a population as being the partial solution to a problem instead of the full solution. Compared with traditional DE, the proposed STMDE algorithm reduces the error by 7. 95, 4.51, 5.22, and 51.34% in terms of regulation performance, noise rejection ability, robustness to parameter variation of the controlled system, and controller tracking capability, respectively. In addition, our experimental results also indicate that the proposed STMDE algorithm exhibits superior performance to other algorithms used for solving nonlinear temperature-sensing control problems.

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“…In this context, the fast and accurate control scheme in complex situations is highly desirable for a USV which pursues high‐accuracy mission efficiency. Recently, some approaches to attenuate complex unknowns have turned to fuzzy logic system, 32‐34 neural networks, 35‐40 and reinforcement learning 41 . However, the aforementioned approximation‐based approaches can only achieve asymptotic/exponential stability with bounded tracking residuals.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time extended state observer‐based exact tracking control of an unmanned surface vehicle

Wang

Zhu

Qin

et al. 2020

Intl J Robust & Nonlinear

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In the presence of complex unknowns consisting of system dynamics and environmental disturbances, it is rather meaningful to exactly track an unmanned surface vehicle (USV) to the desired trajectory in practical scenarios including routing inspection, marine survey, and guard patrol, etc. In this paper, the exact trajectory tracking problem is solved by establishing a finite‐time extended state observer‐ (FESO) based exact tracking control (FESO‐ETC) scheme. By virtue of nonsmooth analysis, the FESO is firstly devised by only requiring continuous differentiability and is incorporated into the nonsingular fast terminal sliding mode control framework, and thereby further enhancing disturbance rejection and tracking accuracy. Moreover, global finite‐time stability of the entire FESO‐ETC closed‐loop system is derived from rigorously theoretical analysis, and thereby contributing to a model‐free finite‐time control paradigm. Simulation studies and comparisons demonstrate that the proposed FESO‐ETC approach can achieve exact trajectory tracking in the presence of complex unknowns.

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Efficient Approach for RLS Type Learning in TSK Neural Fuzzy Systems

Cited by 28 publications

References 46 publications

Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments

Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments

Neural Fuzzy Inference Network Based on Differential Evolution for Nonlinear System Control

Finite‐time extended state observer‐based exact tracking control of an unmanned surface vehicle

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