A QoS-Tunable Scheme for ATM Cell Scheduling Using Evolutionary Fuzzy System

Li, Ju Hui; Lim, Meng‐Hiot; Cao, Qi

doi:10.1007/s10489-005-4608-x

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…, r) are given. Here, the accuracy of the model state estimation system could be improved, whenever the use of FAKF with respect to the traditional extended adaptive Kalman filter (EAKF) is preferred [46][47][48][49][50]. Now, the linear single input-single output model of the system can be described as…”

Section: Fuzzy Adaptive Kalman Filtermentioning

confidence: 99%

Fuzzy predictive control based multiple models strategy for a tubular heat exchanger system

Mazinan

Sadati

2009

Appl Intell

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This work deals with the problem of controlling the outlet temperature of a tubular heat exchanger system by means of flow pressure. The usual industrial case is to try to control the outlet temperature by either the temperature or the flow of the fluid, which flows through the shell tube. But, in some situations, this is not possible, due to the fact that the whole of system coefficients variation cannot quite be covered by control action. In this case, the system behavior must precisely be modeled and appropriate control action needs to be obtained based on novel techniques. A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has been proposed, in this paper. The main idea of the proposed control strategy is to represent the operating environments of the system, which have a wide range of variation with respect to time by multiple explicit linear models. In this strategy, the best model of the system is accurately identified, at each instant of time, by an intelligent decision mechanism (IDM), which is organized based on both new recursive weight generator and fuzzy adaptive Kalman filter approaches. After that, the adaptive algorithm is implemented on the chosen model. Finally, for having a good tracking performance, the generalized predictive control is instantly updated and its control action is also applied to the system. For demonstrating the effectiveness of the proposed approach, simulations are all done and the results are also compared with those obtained using a nonlinear GPC (NLGPC) approach that is realized based on the Wiener model of the system. The results can verify the validity of the proposed control scheme.

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Section: Fuzzy Adaptive Kalman Filtermentioning

confidence: 99%

Fuzzy predictive control based multiple models strategy for a tubular heat exchanger system

Mazinan

Sadati

2009

Appl Intell

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“…Now, for having high accuracy in the fuzzy-based system, the fuzzy set parameters are initially obtained from the GA algorithm [48][49][50][51][52][53][54][55]. The obtained fuzzy sets are also shown in Fig.…”

Section: The Fuzzy-based Adaptive Kalman Filter Approachmentioning

confidence: 99%

An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system

Mazinan

Sadati

2009

Appl Intell

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The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of A.H. Mazinan ( ) Islamic Azad University (IAU), South Tehran Branch, Tehran, Iran

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“…1(b). Functionally, the role of the GA-based evolution module is to evolve a suitable context which is then configured into the CMU of the RcFIP [18]. The RcFIPbased system is capable of maintaining suitable level of performance in a dynamically changing operating environment.…”

Section: Rcfip_3mentioning

confidence: 99%