A Robust Identification Method for Nonlinear Dual Rate Circuit System with Output Time Delay
A system robust and circuit system technology, applied in CAD circuit design, electrical digital data processing, instruments, etc., can solve the problem of low robustness recognition rate and achieve the effect of ensuring accuracy and reliability
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specific Embodiment approach 1
[0024] Specific implementation mode 1: In this implementation mode, a method for robust identification of a nonlinear dual-rate circuit system with output time-delay The specific process is as follows:
[0025] Step 1. Connect the two output voltage ports of the arbitrary waveform generator to the two voltage input ports of the circuit system respectively; the input voltage port and the output voltage port of the circuit respectively pass through the buffer register and connect to the voltage measurement port of the data acquisition card ;Use the arbitrary waveform generator to generate the fast-sampling input voltage signal and the reference scheduling voltage signal of the arbitrary waveform, and input the circuit system to be tested; and use the data acquisition card to collect the fast-sampling input signal, the scheduling signal and the generated slow sampling The output signal, that is, the slow sampling output value;
[0026] Among them, the fast sampling input value, t...
specific Embodiment approach 2
[0032] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the first step, a nonlinear circuit system is established based on the fast sampling input voltage signal, the reference dispatch voltage signal, the slow sampling output value, and the process noise and observation noise The input-output model of , to clarify the problem to be identified, the specific process is:
[0033] Step 11: In order to model the dual-rate sampling and time lag existing in the circuit system, the following linear variable parameter model structure is used to describe it:
[0034] A(w l ,z -1 )x l =B(w l ,z -1 )u l +∈ l ,l=1,2,...,L (1)
[0035] n=1,2,...,N (2)
[0036] Among them, u l Input signal value for fast sampling, w l is the scheduling signal value, ∈ l is the process noise, x l is the fast sampling output signal value, L is the total number of fast sampling points, for T n The output of the fast sampling process at time d ...
specific Embodiment approach 3
[0050]Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in Step 2, under the framework of probability, the specific process of establishing a robust identification model of a nonlinear circuit system is as follows:
[0051] Step 21: Model the observation noise as a Student's t distribution, namely
[0052]
[0053] Among them, ξ is the precision parameter of Student's t distribution, v is the degree of freedom parameter, is the observation noise;
[0054] Step 2 and 2: Introduce the hidden variable τ of the student t distribution into formula (6), and the conditional probability distribution p(y|Λ,τ,ξ;T) of the output signal can be decomposed into the following two formulas:
[0055]
[0056]
[0057] Among them, Λ={λ 1 ,...,λ N} is the importance matrix of time lag, τ={τ 1 ,…,τ N} is the latent variable vector of Student's t distribution; λ n Delayed by a set of binary variables λ nm Composition (m=1,2,...,M), that is, when th...
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