Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants

Abiyev, Rahib H.; Kaynak, Okyay

doi:10.1109/tie.2010.2043036

Cited by 190 publications

(101 citation statements)

References 46 publications

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“…As shown in Table III, IT2IFLS exhibits a low level of RMSE over these evolving T2FLSs. In particular, the performance of IT2IFLS is compared with Type-2 TSK Fuzzy Neural System (Type-2 TSK FNS) [28], TSCIT2FNN [26] and SIT2FNN [29], which also utilised the parameter β to adjust the contribution of upper and lower membership values in their final outputs. The results show a clear performance improvement of IT2IFLS over Type-2 TSK FNS, TSCIT2FNN and SIT2FNN.…”

Section: B System Identificationmentioning

confidence: 99%

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Eyoh

John

Maere

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parameters of the IT2IFLS. The empirical comparison is made on the designed system using two synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in terms of non-membership functions and hesitation indexes in IT2IFLS tend to reduce the root mean square error (RMSE) of the system compared to a type-1 fuzzy logic approach and some interval type-2 fuzzy systems.

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Section: B System Identificationmentioning

confidence: 99%

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Eyoh

John

Maere

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…The proposed scheme guarantees the H1 tracking for SISO time-delay nonlinear systems althought not necessarily NCS. The type-2 fuzzy neural structure was also successfully utilized in [17], where it was used for indentification and control of time-varying plants. The authors of [18] extended the Takagi-Sugeno fuzzy model approach to the stability analysis and controller design for interval type-2 fuzzy systems with time-varying delays.…”

Section: Variable Time Delaysmentioning

confidence: 99%

Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic

Blaho¹,

́n²,

Viszus³

et al. 2015

Journal of Electrical Engineering

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The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of time delays and packet losses. A type-2 fuzzy controller has been designed and compared to a type-1 fuzzy controller. The intervals of type-2 fuzzy controller were optimized via genetic algorithm.K e y w o r d s: networked control systems, packet loss, time delay, true time, type-2 fuzzy logic

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“…The derivation of this condition can be found in [48] and [65] and is given in the Appendix. The learning rate η is within the range [0, 1].…”

Section: B Parameter Learningmentioning

confidence: 99%

“…Some type-2 FNNs [44], [46], [51] are trained using a Kalman filter algorithm to improve the learning performance. In [48], the author proposed a novel type-2 TSKbased fuzzy neural structure (FNS) for the identification and control of dynamic plants. The design of two-axis motion control using an interval type-2 fuzzy set was proposed in [49], and the structure of a discrete interval type-2 fuzzy system by fuzzy c-means (FCM) clustering has been proposed in [50].…”

mentioning

confidence: 99%

A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications

Lin

Chang

Lin

2014

IEEE Trans. Ind. Electron.

134

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Abstract-In this paper, a Takagi-Sugeno-Kang (TSK)-typebased self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN) is proposed for system modeling and noise cancellation problems. A TSCIT2FNN uses type-2 fuzzy sets in an FNN in order to handle the uncertainties associated with information or data in the knowledge base. The antecedent part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the TSCIT2FNN, where compensatory-based fuzzy reasoning uses adaptive fuzzy operation of a neural fuzzy system to make the fuzzy logic system effective and adaptive, and the consequent part is of the TSK type. The TSK-type consequent part is a linear combination of exogenous input variables. Initially, the rule base in the TSCIT2FNN is empty. All rules are derived according to online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by a variable-expansive Kalman filter algorithm to the reinforce parameter learning ability. The antecedent type-2 fuzzy sets and compensatory weights are learned by a gradient descent algorithm to improve the learning performance. The performance of TSCIT2FNN for the identification is validated and compared with several type-1 and type-2 FNNs. Simulation results show that our approach produces smaller root-mean-square errors and converges more quickly.

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Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants

Cited by 190 publications

References 46 publications

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic

A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications

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