Nonlinear Modeling of Switched Reluctance Motor Based on BP Neural Network

Cai, Yan; Gao, Chao

doi:10.1109/icnc.2007.504

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…The existing shortcomings are that the training convergence of BP algorithm is too slow and the BP algorithm is vulnerable to the local minimum [7]. And the accuracy, generalization ability and success of the models depend on the following aspects: adequacy of the training sample selection, the number of hidden-layer neurons and the number of the training steps.…”

Section: The Modeling Based On Bp Neural Networkmentioning

confidence: 99%

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Nonlinear Neural Network-based Modeling of Switched Reluctance Motor

Qin¹,

Shi

Chi

et al. 2010

2010 Asia-Pacific Power and Energy Engineering Conference

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In order to improve the performance of switched reluctance driving system, it is necessary to build an accurate switched reluctance motor (SRM) model. In this paper, a nonlinear flux-linkage model and a torque model of SRM are presented by using the measured accurate flux-linkage data, torque data and nonlinear mapping ability of BP neural network, which is based on fast self-configuring algorithm. In contrast with the traditional models, these two models have the abilities of fast convergence in training, good learning generalization, small network scale and easy real-time control. An experiment is carried out to demonstrate the accuracy and feasibility of the presented models. The result shows that the models have a better accuracy than the previous ones and are good for further optimization of the energy conversion and reducing the torque ripple.

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Section: The Modeling Based On Bp Neural Networkmentioning

confidence: 99%

“…Levenberg-Marquardt is a dedicated method to minimize the sum of the squared deviation and is an approximate of the Newton algorithm [7]. It only needs to calculate the Jacobian matrix, which can avoid the calculation of Hessian matrix and its inverse.…”

Section: The Training Algorithm Of the Bp Networkmentioning

confidence: 99%

Nonlinear Neural Network-based Modeling of Switched Reluctance Motor

Qin¹,

Shi

Chi

et al. 2010

2010 Asia-Pacific Power and Energy Engineering Conference

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“…Some recent publications have used different neural network methods to develop a non-linear SRM [2,4,13,20,21,25,26,28,29]. All these above methods have either faster computational speed or good accuracy but not both.…”

Section: Introductionmentioning

confidence: 99%

Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS

Susitra

Paramasivam²

2014

Journal of Intelligent &Amp; Fuzzy Systems

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A Non-linear flux linkage model of a Switched reluctance Machine (SRM) has been developed using two different real time applicable modelling techniques and presented in this paper. The techniques are based on Multivariate nonlinear regression (MVNLR) and adaptive neuro fuzzy inference system (ANFIS). The best features of MVNLR and ANFIS are utilized in this research to develop the computationally efficient flux linkage model for SRM. Mathematical models for the phase flux linkage ψ(i, θ) using MVNLR and ANFIS have been successfully arrived, tested and presented for various values of phase currents (Iph) and rotor positions (θ) of a non linear SRM. It is observed that MVNLR and ANFIS are highly suitable for flux linkage ψ(i, θ) modelling of SRM which is tested to be in good agreement with the training data used for modelling.

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“…Ref. 6 proposes a modeling method for SRM based on BP neural network (BPNN), which is then used in torque ripple minimization. Ref.…”

Section: Introductionmentioning

confidence: 99%

Accurate modeling of switched reluctance machine based on hybrid trained WNN

Song

et al. 2014

AIP Advances

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According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, the nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.

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Nonlinear Modeling of Switched Reluctance Motor Based on BP Neural Network

Cited by 16 publications

References 4 publications

Nonlinear Neural Network-based Modeling of Switched Reluctance Motor

Nonlinear Neural Network-based Modeling of Switched Reluctance Motor

Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS

Accurate modeling of switched reluctance machine based on hybrid trained WNN

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