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
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Embodiment 1
[0035] The steps of the non-linear analog circuit fault diagnosis method of Wiener kernel and neural network of the present invention:
[0036] (1) First determine the various fault states of the nonlinear analog circuit under test, assuming that there are m kinds of states in total, and establish a fault state set;
[0037] (2) Then sequentially apply Gaussian white noise to the non-linear analog circuit under test in the above fault states as an input signal, and measure the input and output signals at the same time to obtain a sampled data sequence, and obtain each part of the circuit under test through data processing The corresponding first n-order Wiener kernel in the fault state;
[0038] (3) Take the order n of the Wiener kernel obtained in the previous step as the number of input neurons of the neural network, take the number of fault states as the number of output neurons of the neural network, establish a BP neural network, and use each state of the diagnosed system...
Embodiment 2
[0041] The non-linear analog circuit fault diagnosis method of Wiener kernel and neural network described in embodiment 1, in step (1), determine the possible m kinds of fault states of the non-linear analog circuit under test, and carry out numbering, including:
[0042] (a) It is determined that all components of the nonlinear analog circuit under test are in a normal state with nominal parameters;
[0043] (b) Determine the soft fault states such as the actual value of the element in the non-linear analog circuit being tested is too large or too small;
[0044] (c) Determine the hard fault states such as short circuit and open circuit of the components in the tested nonlinear analog circuit;
[0045] (d) Number the various states mentioned above, which are respectively 1, 2, ..., m, where m is a natural number.
Embodiment 3
[0047] The non-linear analog circuit fault diagnosis method of the Wiener kernel and neural network of embodiment 1 or 2, in step (2), the first n order Wiener kernels of each fault state are obtained by following steps:
[0048] (a) Make the non-linear analog circuit under test in fault state 1;
[0049] (b) Apply Gaussian white noise to the above circuit as the input signal, and measure the input and output signals at the same time to obtain the sampling sequence data, and use the method of finding multi-order correlation functions to calculate the Wiener kernel k of each order 10 , k 11 , k 12 , k 13 …k 1n , the specific calculation formula is as follows:
[0050] For nonlinear systems, when the input x(t) is Gaussian white noise, the output y(t) can be expanded into a Wiener series form
[0051] y ( t ) = Σ i = 0 ∞ ...
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