Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

Naghibi, Zohreh; Sadrossadat, Sayed Alireza

doi:10.1002/cta.2631

Cited by 21 publications

(20 citation statements)

References 32 publications

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“…Naghibi, Zohreh and Sadrossadat, Sayed Alireza proposed a new method, called local-global feedback recurrent neural network, which is used to model the dynamic behavior of nonlinear circuits. In addition, the proposed LGFRNN converges faster than existing models in simulation tools [1]. Zhu Quanmin and Wei Cunzhang and others also said that in the design of control systems, neural networks have been widely used as approximate values for nonlinear dynamic factories, but they have almost never been used as appropriate dynamic inverters, especially provided in advance Dynamic model.…”

Section: Related Workmentioning

confidence: 99%

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RPE Algorithm of Recurrent Neural Network and Its Application In Modeling of Nonlinear Dynamic Systems

Pin¹,

Fan²,

Wang³

et al. 2021

DSA

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Recurrent neural network (RNN) has very sensitive feedback characteristics, so it is very suitable for the modeling of nonlinear dynamic systems, but the traditional RNN BP algorithm has problems, such as slow convergence speed and complicated training algorithm, which leads to there are great limitations in its real application. To this end, this paper proposes a recursive neural network for RPE algorithm, explores the principle and calculation process of its algorithm, and analyzes its real-time application in nonlinear dynamic system modeling, hoping to explore a fast recursive neural network learning algorithm. In this paper, through a large number of experiments, using mathematical statistical calculation methods, detailed analysis and discussion of the effects of different types of RNNs and different calculation methods on the modeling of nonlinear dynamic systems. At last, the results of research present that the dynamic recursive neural network takes less time to adjust the parameters, and the error value is only between 0.02-0.036, so that it is more suitable for nonlinear dynamic system modeling; compared with BP algorithm, traditional parameter estimation method-RPE algorithm, combined with RNN for nonlinear to establish the modeling of the dynamic system has optimized its effect, not only the convergence speed is fast, but also the maximum error from the expected output is only 0.01, and the accuracy of the model after construction is extremely high.

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Section: Related Workmentioning

confidence: 99%

“…The principle is to modify the unknown parameter vector along the Gauss-Newton of ( )  E to search direction, so that it tends to the minimum value. The correction algorithm formula of the parameter vector is as (1).…”

Section: )Classificationmentioning

confidence: 99%

RPE Algorithm of Recurrent Neural Network and Its Application In Modeling of Nonlinear Dynamic Systems

Pin¹,

Fan²,

Wang³

et al. 2021

DSA

View full text Add to dashboard Cite

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“…the same modelling technique can be reused for passive/active devices/ circuits) and easier update for ANN models whenever device or component technology changes. Recently, ANNs have been applied to a wide variety of engineering problems such as renewable energy, pattern recognition, biomedical engineering, microwave applications, control and power electrical engineering [22][23][24][25]. In this paper, we compared conventional optimisation method based on analytical formulas with the proposed optimisation method based on models obtained from ANNs.…”

Section: Introductionmentioning

confidence: 99%

ANN‐based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor

Sadrossadat

Rahmani

2020

IET Electric Power Applications

Self Cite

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This study presents a new method for parametric modelling and optimisation of a permanent-magnet brushless DC (BLDC) motor. We proposed an artificial neural network (ANN)-based L P metric technique to combine and optimise different objective functions of a BLDC motor using ANN-based models and compared with conventional optimisation methods with analytical models. To proceed with this optimisation problem, the L P function should be minimised. For applying constraints to this problem, a simple method called penalty factor is proposed, in which a penalty term was added to the L P function when the constraints are violated. We considered three goals in this optimisation: efficiency maximisation, speed maximisation and material cost minimisation. Since the load is constant torque in our case, more speed means more powerful motor, and to achieve the minimum material cost goal the volume of the magnet is set as an objective function. To find the optimum geometric parameters, we used gradient-based method subject to non-linear magnetic constraints. All the obtained results were validated by Ansoft Maxwell. Optimising using the proposed method including ANN-based models does not require knowledge about complicated electric/magnetic equations. Also, ANN-based BLDC motor model is more accurate than analytical models and faster than existing models in simulation tools.

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“…The second one is the Volterra series for nonlinear systems with memory [16]- [19]. The third one is the Recurrent Neural Network (RNN) for complex nonlinear circuits [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…In those approaches, input signals are required to be properly set and many information including frequencies, amplitudes, and phases of output signals should be known. As for the RNN model, a large quantity of data are needed to train the network [20]- [22]. However, for a practical nonlinear circuit, many modules may work simultaneously and the input signals cannot be set arbitrarily.…”

Section: Introductionmentioning

confidence: 99%

A New Electromagnetic Emission Source Frequency and Factor Identification Approach for Nonlinear Circuits From the Output Spectrum Data

Yang

et al. 2020

IEEE Access

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For the practical nonlinear circuits, a number of new frequencies are generated in the output spectrum, which makes electromagnetic interference location quite challenging. To mitigate this problem, an approach is proposed to identify the electromagnetic emission source frequencies and nonlinear factors from the output spectrum of nonlinear circuits. The amplitude of the output spectrum with the input of two sine signals is first calculated based on the power series. Then, the Order Image (OI) is defined to reveal the output spectrum features, and its symmetry characteristic has been comprehensively analyzed. Results show that the maximum symmetry index is observed when the reference frequencies are equal to the excitation frequencies. Based on this observation, the electromagnetic emission source frequencies can be obtained. In addition, a Backward Algorithm (BA) has been introduced to extract nonlinear factors. The proposed approach only requires the output spectrum and the amplitude of one excitation. Compared with other techniques, like the Support Vector Machines and the Recurrent Neural Network, it needs much less prior information. Finally, several examples are carried out to verify the effectiveness of the proposed approach. Results show that the emission source frequencies and nonlinear factors can be accurately extracted. Therefore, the proposed approach shows great potential in electromagnetic interference location and characteristic analysis for nonlinear circuits. INDEX TERMS Electromagnetic emission sources, identification, nonlinear circuits, Order Image, power series.

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Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

Cited by 21 publications

References 32 publications

RPE Algorithm of Recurrent Neural Network and Its Application In Modeling of Nonlinear Dynamic Systems

RPE Algorithm of Recurrent Neural Network and Its Application In Modeling of Nonlinear Dynamic Systems

ANN‐based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor

A New Electromagnetic Emission Source Frequency and Factor Identification Approach for Nonlinear Circuits From the Output Spectrum Data

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