Simulation equalizer optimization design method, system and equipment based on deep learning algorithm and medium

Through deep learning algorithms combined with inverse and forward neural networks, the automated design of simulated equalizer circuits is realized, which solves the problem of time-consuming and labor-intensive design methods and is difficult to deal with complex design standards, improves design efficiency and accuracy, and meets the design needs of complex equalizers.

CN119720906AActive Publication Date: 2025-03-28XIDIAN UNIV
5 Cites 0 Cited by

Patent Information

Application Number
CN202411795184.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-09
Publication Date
2025-03-28
Estimated Expiration
2044-12-09

AI Technical Summary

Technical Problem

Traditional analog equalizer design methods are time-consuming and labor-intensive. Relying on manual calculations is difficult to ensure design quality. In the face of complex circuit design, the design cycle is long and it is difficult to cope with the growing design standards and target parameters.

Method used

The simulation equalizer optimization design method based on deep learning algorithm is adopted. Through the combination of inverse neural network and forward neural network, additional equalizer circuit parameter data are automatically generated, and a new data set is formed with the original simulation data to train the forward neural network, and finally the automatic design and optimization of the simulated equalizer circuit is realized.

Benefits of technology

It significantly reduces manual computing costs, saves computing resources and design time, improves design efficiency and accuracy, and can complete the design of complex equalizer circuits in a very short time, meeting the needs of high performance, wide band, and multiple design indicators.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

A simulation equalizer optimization design method, system, device and medium based on a deep learning algorithm, the method comprises the following steps: training a reverse neural network through an equalizer circuit electromagnetic response curve, and generating extra equalizer circuit parameter data through the trained reverse neural network; the method comprises the steps that a forward neural network is trained through a new data set, a predicted equalizer electromagnetic response curve is output through the trained forward neural network, the predicted equalizer electromagnetic response curve is compared with a target equalizer electromagnetic response curve, and the equalizer electromagnetic response curve is obtained. If the conditions are met, utilizing a reverse neural network to generate equalizer circuit parameters actually meeting the conditions, and completing the design; the system, the device and the medium are used for bearing and implementing the method. According to the method, the design process is optimized through a deep learning method, automation of circuit design is achieved, and design time and calculation complexity are reduced.
Need to check novelty before this filing date? Find Prior Art