An Input–Output Feedback Linearization Control Method Synthesized by Artificial Neural Network for Grid-Tied Packed E-Cell Inverter

Mehrasa, Majid; Babaie, Mohammad; Sharifzadeh, Mohammad; Al‐Haddad, Kamal

doi:10.1109/tia.2021.3049456

Cited by 40 publications

(16 citation statements)

References 32 publications

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“…The premise of this transmission is that electrical impulses at the end of the axon reach a certain level before chemicals are produced to stimulate the next neuron to complete the transmission. Artificial neural network is a mathematical model for deep learning based on the working principle between neurons [15]. This model is modeled according to the composition of the human nervous system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization Method of English Listening Classroom Teaching for College Students Based on Neural Network

2022

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The existing teaching evaluation methods and models have some problems, such as strong subjectivity and randomness, difficulty in determining index weight, over-fitting, slow convergence and weak computing ability. Firstly, this paper states the research background, research status, research questions, research methods, expected research results and innovation points of the subject in detail. Then the relevant theoretical basis of convolutional neural network used in this paper is described. In this paper, a teaching evaluation model based on (BPNN)(BPNN) is established. Taking the actual situation of English listening teaching in a college in this city as an example, the data were collected and preprocessed. Then the structure of BPNN is determined according to the established evaluation system, and the data is input to train the network, and the evaluation result is obtained. Finally, the error analysis of the results is carried out.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization Method of English Listening Classroom Teaching for College Students Based on Neural Network

2022

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“…The machine learning of electricity bill review is mainly carried out by constructing an artificial neural network [3] . The basic neural network consists of three parts: the source node input layer, the hidden neuron layer, and the neuron output layer [4] .…”

Section: Artificial Intelligence Electricity Fee Accounting Model Fra...mentioning

confidence: 99%

Research and application of key technologies for intelligent audit of market-based electricity sales charges based on artificial intelligence

Xie¹,

Liu²,

Yang³

et al. 2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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With the business development of power grid companies and the further deepening of power system reform, the accounting business undertaken by power marketing departments is becoming more and more complex and diversified. This paper analyzes the limitations of the current electricity charge accounting audit in Shandong electric power company, constructs the basic framework, main tasks and operation mode of machine learning for electricity charge abnormal audit, expounds the advantages of electricity charge accounting audit based on artificial intelligence, and puts forward the application prospect of artificial intelligence technology in electricity charge accounting.

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“…Therefore, various approaches using other linearization techniques have been developed to address this requirement. A nonlinear system was transformed into a linear system by the input-output feedback linearization technique [16,17]. A robust feedback linearization technique for an induction motor was proposed [18].…”

Section: Introductionmentioning

confidence: 99%

Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors

Yim

Kim

et al. 2022

Mathematics

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In general, it is not easy work to design controllers and observers for high-order nonlinear systems. Planar motors that are applied to semiconductor wafer-stage processes have 14th-order nonlinear dynamics and require high resolution for position tracking. Thus, many sensors are required to achieve enhanced tracking performance because there are many state variables. To handle these problems, we developed a Lyapunov-based controller to improve the position tracking performance. Consequently, a nonlinear observer (NOB) was also developed to estimate all of the state variables including the position, the velocity, and the phase current using only position feedback. The closed-loop stability is proved through Lyapunov theory and the input-to-state stability (ISS) property. The proposed method was evaluated based on the simulation results and compared with the conventional proportional–integral–derivative (PID) control method to show the improvement in the position tracking performance.

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An Input–Output Feedback Linearization Control Method Synthesized by Artificial Neural Network for Grid-Tied Packed E-Cell Inverter

Cited by 40 publications

References 32 publications

Optimization Method of English Listening Classroom Teaching for College Students Based on Neural Network

Optimization Method of English Listening Classroom Teaching for College Students Based on Neural Network

Research and application of key technologies for intelligent audit of market-based electricity sales charges based on artificial intelligence

Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors

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