Abstract
Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.
Highlights
With the development of system-on-chip (SoC) integrated circuits, the number and diversity of mixed signal circuits using the molds have increased significantly, such as high-speed interactive operating system (IOs), sensors, power supply, clocks and other different circuit forms [1].The manufacturing test procedures are generally divided into the wafer/sequencing test, encapsulation test, stress test/aging test and system test
BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects
The improved modified node algorithm (MNA) is used to build an optimization model for the implementation stage of the circuit pressure test, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test
Summary
With the development of system-on-chip (SoC) integrated circuits, the number and diversity of mixed signal circuits using the molds have increased significantly, such as high-speed interactive operating system (IOs), sensors, power supply, clocks and other different circuit forms [1]. In order to reduce the test time, we introduced the automatic test compression method of analog and mixed signal circuits, and applied the test compression algorithm to the pressure test in this work. For a series of problems in the above electronic circuit testing process, data fusion technology can be used to reduce redundancy and error data, reduce data transmission volume, improve information accuracy and energy and channel utilization, and achieve the purpose of reducing energy consumption. Backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. A simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test
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