Identification of non-linear system structure and parameters using regime decomposition

Johansen, Tor Arne; Foss, Bjarne A.

doi:10.1016/0005-1098(94)00096-2

Cited by 208 publications

(63 citation statements)

References 14 publications

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“…This technique was originally proposed by Dunn [25] and later extended by Bezdek [26]. In this approach, the system's operating space is partitioned into several operating regions, where each rule would represent a local linear model at the corresponding regime [12]. In this case, the TSK fuzzy models are able to approximate the nonlinear systems by performing an interpolation of local linear models via their inference mechanism.…”

Section: B Neuro-fuzzy Structurementioning

confidence: 99%

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Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

Chaibakhsh¹,

Chaibakhsh²,

Rahman³

2011

IJCEA

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Abstract-In this paper, an application of a neuro-fuzzy modeling approach is presented in order to characterize the essential behavior of enzymatic esterification processes. The accuracy of the developed model was validated by comparing the response of the model and actual experimental data. The simulation results showed good generalization of the proposed model and its ability to predict the reaction yield, where the error of prediction for training data was less than 3%, and for validating and testing data less than 3 and 1.5%, respectively. A model-based optimization was performed to obtain the best operating conditions by using genetic algorithm. A fair comparison between the optimization results obtained from simulation experiments and laboratory data indicated the accuracy and feasibility of the proposed approach for estimating the optimal profiles in biotechnological processes. This can further facilitate up-scaling of the process by selecting the appropriate combinations of potential manufacturing parameters.Index Terms-Enzymatic esterification, experimental data, model-based optimization, fuzzy logic system, clustering approach, genetic algorithm.

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Section: B Neuro-fuzzy Structurementioning

confidence: 99%

“…However, employing small datasets for developing fuzzy models may cause over-fitting difficulties. Clustering approaches have been proposed to define the structure of fuzzy systems and to reduce the tunable parameters of fuzzy models with minimum losses in the accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

Chaibakhsh¹,

Chaibakhsh²,

Rahman³

2011

IJCEA

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“…Such approximations are widely used in the literature (see, for instance, [47]). In fact it is shown in [46] that, under certain smoothness properties, the nonlinear system S(p k ) can be approximated to any desired accuracy on a compact subset of the state and input spaces by means of the representation (2.10) for a sufficiently large number of local models.…”

Section: Ftc Problem Formulationmentioning

confidence: 99%

Fault Tolerant Flight Control - A Survey

Verhaegen

Kanev

Hallouzi³

et al. 2010

Lecture Notes in Control and Information Sciences

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“…In these cases, other alternatives, in which non-linear models in predictive control could be used, may result in suitable alternatives to the linear controllers. There are several proposals in this field [1], [20], [29] and [21]. An interesting option is to use neurofuzzy models [22], [23], [24], [25], [26], [27], [28].…”

Section: Fuzzy Generalized Predictive Controlmentioning

confidence: 99%

Neurofuzzy model based predictive control for thermal batch processes

Escaño

Bordons

Vilas

et al. 2009

Journal of Process Control

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In many cases, it is difficult to derive a precise mathematical model, based on first principles, for a given process. Besides, the computation of the solution of models obtained through this methology may require a large computational effort making them useless for real time tasks like control or optimization. Neurofuzzy modelling, which permits an easy way to derive successful models, is a good alternative which can be employed to overcome such limitations. In this paper, together with the neurofuzzy modelling, several strategies based on non-linear predictive control are presented. The low computational cost associated with neurofuzzy models and controllers makes them suitable candidates to be implemented into industrial Programmable Logic Controllers (PLC). Both the model and controllers are validated and implemented in a pilot plant for the thermal sterilization of solid canned food in steam retorts and based on the results, a comparison between the different predictive control strategies is presented.

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Identification of non-linear system structure and parameters using regime decomposition

Cited by 208 publications

References 14 publications

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

Application of Fuzzy Modeling and Optimization in Enzymatic Esterification Process

Fault Tolerant Flight Control - A Survey

Neurofuzzy model based predictive control for thermal batch processes

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