Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Hassan, Saima; Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Jaafar, Jafreezal; Khosravi, Abbas

doi:10.1016/j.asoc.2016.03.023

Cited by 33 publications

(17 citation statements)

References 65 publications

(60 reference statements)

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“…The learning algorithm followed the concept of gradient descent, one of the most popular techniques used to adapt the parameters of IT2FLSs [47], with the loss function L in the form of the mean square error (7) to be minimized.…”

Section: ) Learning Algorithmmentioning

confidence: 99%

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

Herman

Prasad

McGinnity

2017

IEEE Trans. Fuzzy Syst.

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Abstract-One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification. commonly exploited soft computing paradigms in pattern recognition [1]. More specifically, fuzzy methodology has enjoyed considerable popularity in the problem domains where multifarious manifestations of uncertainty are particularly difficult to capture analytically. Brain signal pattern recognition, which lies within the focus of this work, serves as the prime instance in this regard. Consequently, the literature reports the abundance of FL based approaches to analysis, interpretation and classification of brain neurophysiologic data, especially electroencephalogram (EEG), e.g. [2]- [7]. This paper addresses a particularly challenging task of discriminating patterns in the EEG signals recorded from subjects performing motor imagery (MI), which has already received some attention in fuzzy pattern recognition [3], [4] [8]- [11]. The outcome of these investigations has intrinsic implications for the broader field of EEG-based brain-computer interface (BCI). The significance of BCI technology is reflected in its contribution to the improvement of living standards for people affected from neuromuscular disorders [12], [13] and, more recently, in its potential to support a range of rehabilitative therapies [14], [15] as well as the growing impact on computer games industry [16]. One of the key challenges in the field of...

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Section: ) Learning Algorithmmentioning

confidence: 99%

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

Herman

Prasad

McGinnity

2017

IEEE Trans. Fuzzy Syst.

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“…More in-depth TR methods are discussed in [12]. For the design of IT2 FLSs, please see a recent survey [36].…”

Section: Nie-tan Methodsmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

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“…And he also reviewed design and optimization of interval type-2 fuzzy controllers [40], [41], type-2 fuzzy logic applications in clustering, classification and pattern recognition [42], [43] and interval type-2 fuzzy logic applications in intelligent control [44]. There were some others reviews on type-2 fuzzy logic systems, such as reviews on industrial applications of type-2 fuzzy sets and systems [45], type-reduction of type-2 fuzzy sets reviews [46], reviews on optimal design of adaptive type-2 neuro-fuzzy systems [47].…”

Section: Introductionmentioning

confidence: 99%

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Zhong

2020

IEEE Access

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Fractional order PID controller was received attentions in control problems for it had 2 freedom adjustable parameters. Practices had proved that better results could be obtained by introduction of fractional order PID in control problems. And in recently years, fractional order PID combined with fuzzy logic system was gradually got attention, the typical of which was fractional order type-1 fuzzy PID controller. For fractional order PID and fractional order type-1 fuzzy PID controller couldn't deal with the uncertainty of system, so fractional order interval type-2 fuzzy PID controller was applied to solve this problem. But interval type-2 fuzzy sets were a simplification of type-2 fuzzy sets, and there may be have the defect of information loss. A fractional order general type-2 fuzzy PID controller was proposed in this article. The proposed controller based on general type-2 fuzzy logic system, which can make full use of the advantages of general type-2 fuzzy logic system in describing the uncertainty of the system. The fractional order general type-2 fuzzy PID controller utilized a simplified type reduction called NT type reduction algorithm. The NT type reduction algorithm can get the defuzzification result directly and avoided iterative process as KM type reduction commonly used in interval type-2 fuzzy controller. The simulations of 3 processes and a practical inverted pendulum system show that fractional order general type-2 fuzzy PID controller can reduce overshoot, improve system response speed and accelerate system stability time in comparing with other controllers. Especially, when the system has disturbance, parameters uncertainty or structure uncertainty, the fractional order general type-2 fuzzy PID controller has better control effects than other compared controllers. INDEX TERMS Fractional order control, fractional order fuzzy PID, general type-2 fuzzy sets, general type-2 fuzzy logic system, type reduction.

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Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Cited by 33 publications

References 65 publications

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

Review of Recent Type-2 Fuzzy Controller Applications

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

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