Sliding mode-based adaptive learning in dynamical-filter-weights neurons

Sira‐Ramírez, Hebertt; Colina-Morles, Eliezer; Rivas, Francklin

doi:10.1080/002071700403448

Cited by 9 publications

(5 citation statements)

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“…In recent years, a continuous time sliding mode feedback adaptive learning algorithm has been proposed (Sira-Ramirez et al, 2000). In this paper, the sliding mode strategy has been used in the context of output signal tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Development and implementation of a novel adaptive filter for non-linear dynamic system identification

Sahu

Dash

2011

IJAAC

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This paper presents a novel approach for the identification of nonlinear dynamic systems using dynamical filter weight neuron architecture. A sliding mode strategy is proposed for the synthesis of an adaptive learning algorithm for the neuron, whose weights comprise the first-order dynamic filters with adjustable parameters. This approach is known to exhibit robust characteristics and fast convergence properties. Experimental results on nonlinear dynamic systems, governed by difference equations, demonstrate the effectiveness of the proposed approach. Further, a meaningful comparison has been presented with a recent system identification technique that uses Karhunen-Loeve transform approach. The identification error comparison exhibits the robustness and reduced computational cost of the improved sliding mode filter weight algorithm over the later.

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Section: Introductionmentioning

confidence: 99%

Development and implementation of a novel adaptive filter for non-linear dynamic system identification

Sahu

Dash

2011

IJAAC

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“…In the first group, the robustness of SMC is used to obtain better training architectures for SC methods (see, for example, Efe et al, 2002). Sira-Ramirez et al (2000) have demonstrated the use of ANNs with a SMC theory based learning strategy. Huang et al (2003) the weights are adjusted on-line by using the reaching condition of the prespecified sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Sliding Surface Design with Support Vector Machine Based Initial Condition Adaptation

Tokat

2006

Journal of Vibration and Control

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In this paper, the incorporation of soft computing methodologies into sliding mode control structures with a time-varying sliding surface is considered. One of the main problems in the time-varying sliding surface design problem is the determination of the proper values of the design parameters, which are initial condition dependent. When the initial conditions of the system change, the parameters of the time-varying sliding surface have to be calculated again to obtain the desired performance. Therefore, a support vector machine structure is used for estimation of the parameters of the time-varying sliding surface to respond to a change in the system initial conditions, paving the way for a new integration of soft computing methodologies with sliding mode control theory. Simulations are then performed on a second order nonlinear model with an external disturbance and parameter variations to demonstrate the validity of the proposed method. It is illustrated that the sliding surface with the estimated system parameters performs better in moving from different initial conditions to the sliding phase than does the conventional sliding mode controller.

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“…Parma et al (1998) use the SMC technique in the parameter tuning process of a multilayer perceptron. The latest studies towards the integration of SMC and parameter tuning mechanisms have shown that the tuning can be implemented on dynamic weight lter neurons (Sira-Ramirez et al, 2000), and on the parameters of a controller (Efe et al, 2000). An extensive review is provided in the recent survey of Kaynak et al (2001).…”

Section: Introductionmentioning

confidence: 99%

A novel error critic for variable structure control with an ADALINE

Efe

2002

Transactions of the Institute of Measurement and Control

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This paper discusses a novel method for variable structure control. The method aims to create and maintain a prede ned sliding motion in the phase space by adjusting the parameters of an ADALINE-type controller. The objective of the paper is to demonstrate that the task can be achieved under the assumption that the plant under control could be driven towards the sliding manifold. The approach presented assumes the structure of the dynamic representation of the plant is known but the analytical details embodying it are unavailable. The results obtained con rm the prescribed form of tracking claim together with low computational cost due to the adjustment of the controller parameters.

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Sliding mode-based adaptive learning in dynamical-filter-weights neurons

Cited by 9 publications

References 0 publications

Development and implementation of a novel adaptive filter for non-linear dynamic system identification

Development and implementation of a novel adaptive filter for non-linear dynamic system identification

Time-Varying Sliding Surface Design with Support Vector Machine Based Initial Condition Adaptation

A novel error critic for variable structure control with an ADALINE

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