Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

Ahmed, Sevil; Shakev, Nikola; Topalov, Andon V.; Shiev, Kostadin; Kaynak, Okyay

doi:10.1007/s12530-012-9053-6

Cited by 21 publications

(6 citation statements)

References 20 publications

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“…The identification performance of the proposed learning algorithm has been compared with GD, particle swarm optimization (PSO), SMC theory-based online learning for T1FNNs, and the extended sliding mode on-line algorithm for T2FNN presented in [24]. It is to be noted that even if the network structure is the same with the one in [24], the proposed learning rules in this investigation are completely novel and fully sliding mode. In all the examples in this section, the network is designed with three inputs and one output.…”

Section: Simulation Studiesmentioning

confidence: 99%

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Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

Kayacan

Khanesar

2015

IEEE Trans. Ind. Electron.

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In order to achieve faster and more robust convergence (especially under noisy working environments), a sliding mode theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks in this paper. Differently from recent studies, where sliding mode control theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully sliding mode parameter update rules for both the premise and consequent parts of the type-2 fuzzy neural networks. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.

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Section: Simulation Studiesmentioning

confidence: 99%

“…On the other hand, when a step input u(k) < 0.83 is applied to the system, the possible maximum output of the system is approximately equal to 2.26. Thus, the input signal in this investigation has the following form [24]:…”

Section: A Example 1: Identification Of a Non-bibo Nonlinear Plantmentioning

confidence: 99%

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

Kayacan

Khanesar

2015

IEEE Trans. Ind. Electron.

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“…To achieve better control performance, the high-order sliding surface from [37] and [38] is applied as…”

Section: Parameter Learning and Compensator Controller A Parameter Learning For Sorit2pfcmentioning

confidence: 99%

Self-Organizing Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

2019

IEEE Access

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This paper presents a design of a self-organizing recurrent interval type-2 Petri fuzzy controller (SORIT2PFC) for controlling the time-varying delay systems. The control system comprises of a main controller and a compensation controller; the SORIT2PFC as the main controller is used to imitate an ideal controller, and the simple fuzzy compensator controller is used to eliminate the residual error. By using a self-organizing algorithm, the structure of the proposed network can automatically achieve optimal construction. Recurrent neural network and fuzzy Petri nets are applied to improve system performances and to reduce computational burden. Online tune adaptive laws of the proposed controller are derived by implementing the gradient descent method and the Lyapunov stability theorem. Finally, the control efficacy and effectiveness of the proposed controller are verified by the numerical simulations of the time-varying delay systems.INDEX TERMS Type-2 fuzzy system, Petri-fuzzy, recurrent neural network, self-organizing algorithm, time-varying delay systems.

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“…Furthermore, the stability of the learning process is not guaranteed as the error surface includes many local minima and the tuning process may easily get stuck into one of these [41]. As a remedy, SMC theory-based algorithms have been proposed for the adaptation of the neural networks' weights and effectively used on a variety of applications yielding robust system response in handling the uncertainties and imprecision and faster convergence than the traditional learning techniques [28,42,[31][32][33]. The underlying idea of this approach is to restrict the motion of the system in a plane referred to as the sliding surface, where the predefined function of the error is zero [43].…”

Section: Sliding Mode Control Theorymentioning

confidence: 99%

“…The shortcomings with the gradient descent-based and evolutionary approaches give rise to the development of sliding mode control (SMC) theory-based algorithms for the parameter adaptation of the neuro-fuzzy systems [26][27][28][29][30][31][32][33], by means of which robust system response in handling the uncertainties and imprecision and faster convergence than the traditional learning techniques in online tuning can be achieved. In this study, a sliding mode theory-based supervised training algorithm that implements fuzzy reasoning on a spiking neural network is developed and tested for the trajectory control problem of a robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm

Oniz

Kaynak

2015

Neurocomputing

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Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

Cited by 21 publications

References 20 publications

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

Self-Organizing Recurrent Interval Type-2 Petri Fuzzy Design for Time-Varying Delay Systems

Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm

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