Method for diagnosing faults of nonlinear analog circuit based on Wiener kernels and neural network

A technology for simulating circuit faults and neural networks, which is applied in the field of pattern recognition of nonlinear analog circuits, and can solve the problems that Volterra series are not mutually orthogonal and cannot be expanded by Volterra series, so as to improve diagnostic efficiency, wide adaptability, and accuracy. high degree of effect
CN101813747AInactive Publication Date: 2010-08-25哈尔滨海恒博涵科技发展有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
哈尔滨海恒博涵科技发展有限公司
Publication Date
2010-08-25
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention provides a method for diagnosing faults of a nonlinear analog circuit based on Wiener kernels and a neural network. The existing nonlinear systems are difficult to describe mathematically and lack uniform description method. The invention relates to feature extraction, mode recognition and fault diagnosis technology of the nonlinear analog circuit and is characterized by determining a fault state set of the nonlinear analog circuit to be tested; obtaining the first n-order Wiener kernel of each fault state in sequence; establishing a BP neural network and training the neural network with each state code and the corresponding n-order Wiener kernel of the state code; and obtaining the first n-order Wiener kernel of the circuit to be diagnosed and using the kernel as input of the neural network and output of the neural network as the result of diagnosis. By the method, the features of part of nonlinear circuits with Volterra series unable to be described can be extracted, the terms of output and expanded series are orthogonal, feature extraction and data processing are simpler, the diagnostic system has strong generalization capability, and the method is high in accuracy and strong in practicability. The method is used for diagnosing the faults of electronic circuits.
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Description

Technical field:

[0001] The invention relates to pattern recognition, feature extraction method and fault diagnosis of nonlinear analog circuit, in particular to extraction of Wiener kernel, establishment and training of neural network and fault diagnosis method of nonlinear analog circuit. Background technique:

[0002] With the development of digital technology and the improvement of integration technology, the proportion of analog circuits in hybrid circuits is getting smaller and smaller, but analog circuits cannot be replaced, and the links connected with specific processes must use analog circuits. Although the proportion of analog circuits is small, the faults caused by analog circuits are much higher than those caused by digital circuits. However, the diagnostic theory of analog circuits, especially nonlinear analog circuits, is not yet perfect. Therefore, there is an urgent need for a good fault diagnosis method for analog circuits. The method of the present appli...

Claims

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