<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.</p>
</abstract>

Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the...

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 16. Apr. 2022

Akzeptiert: 10. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00140

Schlüsselwörter
Electric energy, High-tech information technology, Power electronic circuit equipment, BP neural network, Circuit operation error

© 2023 Zhiwei Dong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 16. Apr. 2022

Akzeptiert: 10. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00140

SchlüsselwörterElectric energy, High-tech information technology, Power electronic circuit equipment, BP neural network, Circuit operation error

© 2023 Zhiwei Dong, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Electric energy, High-tech information technology, Power electronic circuit equipment, BP neural network, Circuit operation error