Analysis of using RLS in neural fuzzy systems

Yeh, Jen-Wei; Su, Shun‐Feng; Rudas, Imre J.

doi:10.1109/icsmc.2011.6083937

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…We continue the simulations in [19]. In the case of sinc, 7 and 25 rules are considered for two type fuzzy set membership functions with initial variance value 0.1 and 1.…”

Section: Analysis Of the Use Of Rlsmentioning

confidence: 99%

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On Neural Fuzzy Systems

Yeh²

2014

International Journal of Fuzzy Logic and Intelligent Systems

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Neural fuzzy system (NFS) is basically a fuzzy system that has been equipped with learning capability adapted from the learning idea used in neural networks. Due to their outstanding system modeling capability, NFS have been widely employed in various applications. In this article, we intend to discuss several ideas regarding the learning of NFS for modeling systems. The first issue discussed here is about structure learning techniques. Various ideas used in the literature are introduced and discussed. The second issue is about the use of recurrent networks in NFS to model dynamic systems. The discussion about the performance of such systems will be given. It can be found that such a delay feedback can only bring one order to the system not all possible order as claimed in the literature. Finally, the mechanisms and relative learning performance of with the use of the recursive least squares (RLS) algorithm are reported and discussed. The analyses will be on the effects of interactions among rules. Two kinds of systems are considered. They are the strict rules and generalized rules and have difference variances for membership functions. With those observations in our study, several suggestions regarding the use of the RLS algorithm in NFS are presented.

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“…We continue the simulations in [19]. In the case of sinc, 7 and 25 rules are considered for two type fuzzy set membership functions with initial variance value 0.1 and 1.…”

Section: Analysis Of the Use Of Rlsmentioning

confidence: 99%

“…However, in practical applications, there are problems and then various remedy mechanisms may be needed while using RLS for the learning process in NFS. We have analyzed the effects of those approaches in our previous work [18,19]. In this article, the interaction between rules on consequent part and RLS algorithm will be analyzed.…”

Section: Introductionmentioning

confidence: 99%

On Neural Fuzzy Systems

Yeh²

2014

International Journal of Fuzzy Logic and Intelligent Systems

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Learning analysis for correlation of fuzzy rules in applying RLS for neural fuzzy systems

Yeh

2012

2012 IEEE International Conference on Granular Computing

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A Policy Iteration Approach for Flock Motion Control

Abouheaf

Gueaieb

et al. 2021

2021 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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The flocking motion control is concerned with managing the possible conflicts between local and team objectives of multi-agent systems. The overall control process guides the agents while monitoring the flock-cohesiveness and localization. The underlying mechanisms may degrade due to overlooking the unmodeled uncertainties associated with the flock dynamics and formation. On another side, the efficiencies of the various control designs rely on how quickly they can adapt to different dynamic situations in real-time. An online model-free policy iteration mechanism is developed here to guide a flock of agents to follow an independent command generator over a time-varying graph topology. The strength of connectivity between any two agents or the graph edge weight is decided using a position adjacency dependent function. An online recursive least squares approach is adopted to tune the guidance strategies without knowing the dynamics of the agents or those of the command generator. It is compared with another reinforcement learning approach from the literature which is based on a value iteration technique. The simulation results of the policy iteration mechanism revealed fast learning and convergence behaviors with less computational effort.

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Analysis of using RLS in neural fuzzy systems

Cited by 4 publications

References 11 publications

On Neural Fuzzy Systems

On Neural Fuzzy Systems

Learning analysis for correlation of fuzzy rules in applying RLS for neural fuzzy systems

A Policy Iteration Approach for Flock Motion Control

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