Discussion on Accuracy of Approximation with Smooth Fuzzy Models

Sadjadi, Ebrahim Navid; Ebrahimi, Maryam; Gachloo, Zahra

doi:10.1109/ccece47787.2020.9255815

Cited by 9 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently some new smooth compositions have been presented that are able to approximate the derivative of the plant process with accuracy [15]. Many of the contributions in the field for smooth modeling purpose of the dynamical systems have employed the fuzzy relational modeling framework (see [15][16][17] and the references therein). They have been employed for different purposes including modeling static input-output mapping of dynamical systems and for data clustering.…”

Section: International Journal Of Fuzzy Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Model Identification and Self Learning with Smooth Compositions

Sadjadi

García

López

et al. 2019

Int. J. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

This paper develops a smooth model identifica-10 tion and self-learning strategy for dynamic systems taking 11 into account possible parameter variations and uncertain-12 ties. We have tried to solve the problem such that the 13 model follows the changes and variations in the system on 14 a continuous and smooth surface. Running the model to 15 adaptively gain the optimum values of the parameters on a 16 smooth surface would facilitate further improvements in 17 the application of other derivative based optimization 18 control algorithms such as MPC or robust control algo-19 rithms to achieve a combined modeling-control scheme. 20 Compared to the earlier works on the smooth fuzzy mod-21 eling structures, we could reach a desired trade-off between 22 the model optimality and the computational load. The 23 proposed method has been evaluated on a test problem as 24 well as the non-linear dynamic of a chemical process. 25 26 Keywords Fuzzy control Á Fuzzy IF-THEN systems 27 (TSK) Á Smooth compositions 28 1 Introduction 29 Soft computing methods are being used for identification of 30 non-linear and complex systems based on the input-output 31 data collected from the original system [1]. There are many

show abstract

Section: International Journal Of Fuzzy Systemsmentioning

confidence: 99%

“…However, other fuzzy compositions also have been introduced in the literature, which will be reviewed in this section. Let's start with basic definitions in fuzzy compositions: t-norm and s-norm [15,24].…”

Section: Preliminaries On Smooth Fuzzy Compositionsmentioning

confidence: 99%

Fuzzy Model Identification and Self Learning with Smooth Compositions

Sadjadi

García

López

et al. 2019

Int. J. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, some new smooth compositions have been presented and have been employed for modeling static input-output mapping of the dynamical systems in [2,18,19]. All the contributions in application of smooth compositions for advanced control of dynamic systems have employed the relational modeling framework [20].…”

Section: Smooth Fuzzy Compositionsmentioning

confidence: 99%

“…The mostly used fuzzy composition (sometimes called s-t composition) is max-min. However, other fuzzy compositions also have been introduced in the literature [18,23] and some has been collected in Table 1. We would refer the smooth composition II as ''atan'' composition and the smooth composition III as ''acos'' composition, according to the mathematical definition, in the rest of the paper.…”

Section: Problem Definitionsmentioning

confidence: 99%

“…Remark 3 During the fuzzy adaptation process, the membership functions represent linguistic terms of smooth TS fuzzy model interference, which are comprehensible to a human. This aspect, which has been forgotten in the earlier works using relational fuzzy models [18,20,23], is one of the strengths of fuzzy modeling. Actually, the blind performance index used at the relational smooth fuzzy modeling-based tuning of the membership functions of the earlier works causes semantically meaningless linguistic terms at the model interfaces.…”

Section: Corollarymentioning

confidence: 99%

See 1 more Smart Citation

How Effective are Smooth Compositions in Predictive Control of TS Fuzzy Models?

Sadjadi

Menhaj

Herrero

et al. 2019

Int. J. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

In this article, we study the structural properties that smooth compositions bring to predictive control of TS fuzzy models and examine how they affect the uncertainties, parameter variations of the system and environmental noises to die out. We have employed the smoothness structure of compositions to convert the MPC cost function of TS fuzzy model of the nonlinear systems to an incremental iterative algorithm. Hence, the proposed algorithm does not linearize the nonlinear dynamics, neither requires solving an NP optimization problem in MPC and, therefore, is very fast and simple. The connectivist identification-MPC approach-can be employed for the systems with the long-range horizons. Therefore, the technique is beneficial to the dead-time and non-minimum phase systems. The stability analysis of the algorithm has been carried out, and the performance of the smooth TS fuzzy identification-controller scheme to the classical ones has been compared on a non-min phase test problem with different uncertainties and working environments, including (a) the normal working conditions, (b) with the additive noises, (c) with the parametric changes, (d) with the additive time-varying disturbances to demonstrate the robust behavior of the smooth compositions.

show abstract

The recovery plans at the time of COVID-19 foster the journey toward smart city development and sustainability: a narrative review

Sadjadi

2024

Environ Dev Sustain

View full text Add to dashboard Cite

Discussion on Accuracy of Approximation with Smooth Fuzzy Models

Cited by 9 publications

References 14 publications

Fuzzy Model Identification and Self Learning with Smooth Compositions

Fuzzy Model Identification and Self Learning with Smooth Compositions

How Effective are Smooth Compositions in Predictive Control of TS Fuzzy Models?

The recovery plans at the time of COVID-19 foster the journey toward smart city development and sustainability: a narrative review

Contact Info

Product

Resources

About