1998
DOI: 10.1109/19.744205
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A new symbolic method for analog circuit testability evaluation

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Cited by 24 publications
(19 citation statements)
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“…Therefore, the linear combination matrix will no longer have its minimal form. Fig.1 is the nonlinear analog circuit under test, the normal value of each components is:R 1 =R 2 =R 3 =R 4 =R 5 =R 6 =R 7 =R 8 =R 9 =1 ,C 1 =5mF,C 1 =0.5mF dc current is 4A ac current is 10mA.…”
Section: Optimum Computation Of Ambiguity Groupmentioning
confidence: 99%
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“…Therefore, the linear combination matrix will no longer have its minimal form. Fig.1 is the nonlinear analog circuit under test, the normal value of each components is:R 1 =R 2 =R 3 =R 4 =R 5 =R 6 =R 7 =R 8 =R 9 =1 ,C 1 =5mF,C 1 =0.5mF dc current is 4A ac current is 10mA.…”
Section: Optimum Computation Of Ambiguity Groupmentioning
confidence: 99%
“…We will extend this result to ranks of all sub-matrices of the testability matrix that are used to determine the existence of ambiguity groups. Under the assumption we may study properties of the linear combination matrix C 1 considering its equivalent binary matrix D [6] that has the same size as C 1 . An element of the matrix D is equal to one if the corresponding element of C 1 is nonzero, all other elements are set to zero.…”
Section: Canonical Form Of Testability Matrixmentioning
confidence: 99%
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“…Assuming that the faults are expressed as parameter variations without influencing the circuit topology, the testability value T is given by the maximum number of linearly independent columns of Jacobian matrix associated with the fault diagnosis equations [7]. Because testability is related to topology of CUT and the selected test node set and is independent of component values, a new symbolic method for analog circuits' testability evaluation is presented by G. Fedi [8]. The network transfer functions expressed in the following way:…”
Section: A Testability and Ambiguity Groupsmentioning
confidence: 99%
“…A further aspect to take into account for the correct identification of the model is the "solvability" of the model with respect to the parameters chosen for the identification. To this aim, a preliminary evaluation of the testability of the lumped model is performed for determining the solvability degree with respect to the circuit parameters [9]. If the proposed lumped model is not suitable or not complete, the neural learning process does not converge.…”
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confidence: 99%