Fuzzy modelling using a new compact fuzzy system: A special application to the prediction of the mechanical properties of alloy steels

Zhang, Bin; Mahfouf, Mahdi

doi:10.1109/fuzzy.2011.6007730

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…In material engineering and mechanical engineering, it is essential to establish accurate and reliable prediction models for various material behaviours [26,27]. But it may be 'tricky' to precisely describe these behaviours using mathematical models alone due to the complexity of materials' chemical composites and their underlying physical processing mechanisms, such as heat treatment and machining.…”

Section: Applications To Engineering Materialsmentioning

confidence: 99%

Model fusion using fuzzy aggregation: Special applications to metal properties

Zhang

Mahfouf

Yates

et al. 2012

Applied Soft Computing

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Section: Applications To Engineering Materialsmentioning

confidence: 99%

Model fusion using fuzzy aggregation: Special applications to metal properties

Zhang

Mahfouf

Yates

et al. 2012

Applied Soft Computing

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“…In addition, this paper also serves as an initial study towards the development of the evolutionary fuzzy controller for a flapping wing aircraft. Known as the robust universal approximator [19], fuzzy systems allow complex systems to be approximated using a set of fuzzy 'If-Then' rules, allowing better robustness and flexibility, compared to the traditional model-based control systems, whose performance completely relies on the accuracy of the proposed mathematical model in the form of the transfer functions or the state space equations [20], [21]. It is clear that traditional mathematical models often fail to capture the entire behaviours of complex dynamical systems, such as those due to strong non-linearity and time-varying characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, there are other advantages of employing fuzzy systems for modelling and control of complex systems. Considering their intuitive nature [19], deeper insight into the behavior of the systems could be drawn, even from the perspective of the non-expert users (e.g. average drone operators).…”

Section: Introductionmentioning

confidence: 99%

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Entropy Fuzzy System Identification for the Dynamics of the Dragonfly-like Flapping Wing Aircraft

Santoso

Garratt

Anavatti

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this work we present non-linear system identification for a class of the dragonfly-like flapping wing aircraft. We model the system in its vertical and all attitude loops (roll, pitch, and yaw) as well as its actuator dynamics. Based on a set of input-output data, obtained from first principle modelling; we perform the entropy fuzzy system identification to derive the open loop dynamics of the aircraft using the Mamdani Fuzzy inference method, which is more intuitive, despite being non-linear. This will make the proposed models well-suited to non-expert users (e.g. average drone operators). Our research indicates that the information entropy is very effective to maximize the system accuracy while avoiding overfitting problems. Through numerical simulation, we demonstrate the efficacy of the proposed fuzzy models as we can achieve reasonably good average modelling accuracy of around 90 % for all attitude loops.

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“…Fuzzy rule-based modelling has been widely applied to nonlinear, high-dimensional complex real-world problems as it is often necessary to extract and represent knowledge from data when knowledge from other sources is not available or is inadequate [1]- [3]. Recently, developments [4]- [7] in and applications [8]- [10] of type-2 fuzzy logic systems (T2FLSs) have attracted worldwide attention.…”

Section: Introductionmentioning

confidence: 99%

A new computationally efficient mamdani interval type-2 fuzzy modelling framework

Wang

Mahfouf

2012

2012 IEEE International Conference on Fuzzy Systems

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A modified center-of-sums (mCoS) type-reduction technique is proposed in this paper for constructing a data-driven Mamdani interval type-2 fuzzy modelling (MIT2FM) framework. The mCoS type-reducer is an extension of its type-1 counterpart, the center-of-sums defuzzification, which takes both the area of the scaled consequent membership function of each fired rule and its associated geometric center into account for computing the final output. Contrary to the normal center-of-sums typereduction, the proposed approach considers the full area under the scaled consequent membership functions even if such area extends beyond the range of the output variable. This enables the commonly used Gaussian interval type-2 membership functions to be utilised in MIT2FM, the area of which has to be calculated via the improper integrals over the whole real line. Moreover, the mCoS method can make use of the mean of Gaussian membership functions directly, instead of computing the geometric center for each rule, so as to further reduce the computational burden of type-reduction. Compared with the state-of-the-art type-reducers, mCoS is shown to be more efficient and, therefore, makes the interval type-2 based fuzzy logic systems more competitive for data-driven fuzzy modelling applications. In order to test the validity of mCoS type-reduction and the elicited fuzzy modelling scheme, experiments are conducted on a benchmark problem of non-linear time series, where collected data are disturbed by noise, and on the real-world application, namely the prediction of mechanical properties of alloy steels. The mCoS based interval type-2 fuzzy modelling approach is shown to handle uncertainties very well and to provide desired generalisation capability when addressing large high-dimensional data sets.

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Fuzzy modelling using a new compact fuzzy system: A special application to the prediction of the mechanical properties of alloy steels

Cited by 5 publications

References 33 publications

Model fusion using fuzzy aggregation: Special applications to metal properties

Model fusion using fuzzy aggregation: Special applications to metal properties

Entropy Fuzzy System Identification for the Dynamics of the Dragonfly-like Flapping Wing Aircraft

A new computationally efficient mamdani interval type-2 fuzzy modelling framework

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