Abstract
A linear ridgelet network combining ridgelet, linear term and the standard feed-forward neural network used for diagnosing the faults of analog circuit is constructed, and a training algorithm based on the steepest gradient descent method and momentum method for this network and the procedure for diagnosing these faults are proposed. The resulting linear ridgelet network can learn more rapidly from training samples and handle more effectively the complicated fault information of circuit under test than wavelet network and ridgelet network, classifying these faults efficiently and correctly and achieving a high classification accuracy. The simulation results demonstrate the effectiveness of this approach.
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References
Yang S Y. Reliability Design and Fault Diagnosis of Analog System. Beijing: Tsinghua University Press, 1993
Bandler J W, Salama A E. Fault diagnostic of analog circuits. Proc IEEE, 1985, 73: 1279–1325
Spina R, Upadhyaya S. Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Trans Circ Syst, 1997, 44: 188–196
Aminian M, Aminian F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circ Syst, 2000, 44: 151–156
Aminian F, Aminian M, Collins H W. Analog fault diagnosis of actual circuits using neural networks. IEEE Trans Instrum Meas, 2002, 51: 544–550
He Y G, Tan Y H, Sun Y C. Wavelet neural network approach for fault diagnosis of analog circuits. IEE Proc Circ Devic Syst, 2004, 151: 379–384
Candes E J. Ridgelets: theory and applications. Ph.D. Thesis, Department of Statistics, Stanford University, 1998
Candes E J. Harmonic analysis of neural netwoks. Appl Comput Harm Anals, 1999, 30: 197–218
Candes E J, Donoho D L. Ridgelets: a key to high dimensional intermittency? Philos Trans R Soc Lond A, 1999, 357: 2495–2509
Donoho D L. Orthonormal ridgelets and linear singularities. SIMA J Math Anal, 2000, 31: 1062–1099
Candes E J. Ridgelets and the representation of mutilated sobolev functions. SIAM J Math Anal, 1999, 33: 2495–2509
Yang S, Wang M, Jiao L. A new adaptive ridgelet neural network. Lecture Notes in Computer Science. New York: Springer-Verlag, 2005, 3496: 385–391
Yang S, Wang M, Jiao L. Ridgelet kernel regression. Neurocomputing, 2007, 70: 3046–3055
Yang S Y, Wang M, Jiao L C. Incremental constructive ridgelet neural network, Neurocomputing, 2008, 72: 367–377
Hou B, Liu F, Jiao L C. Linear feature detection based on ridgelet. Sci China Ser E-Tech Sci, 2003, 46: 141–152
Do M N, Vetterli M. The finite ridgelet transform for image representation. IEEE Trans Imag Process, 2003, 12: 16–28
Zhang Q, Benveniste A. Wavelet networks. IEEE Trans Neural Network, 1992, 35: 889–898
Chui C K. An Introduction to Wavelets. New York: Academic Press, 1992
Mallat S. A Wavelet Tour of Signal Processing. 2nd ed. New York: Academic Press, 1999
Yang R M, Xu C. A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization. Sci China Ser F-Inf Sci, 2008, 51: 1039–1054
Zhao R Z, Liu X Y, Li C C, et al. Wavelet denoising via sparse representation. Sci China Ser F-Inf Sci, 2009, 52: 1371–1377
Bishop C M. Neural Networks for Pattern Recognition. New York: Oxford Univ. Press, 1995
Gupta M M, Jin L, Homma N. Static and Dynamic Neural network. New Jersey: IEEE Press, 2003
Simon H. Neural Networks: a Comprehensive Foundation. New York: Macmillan, 1994
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Xiao, Y., He, Y. A linear ridgelet network approach for fault diagnosis of analog circuit. Sci. China Inf. Sci. 53, 2251–2264 (2010). https://doi.org/10.1007/s11432-010-4077-7
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DOI: https://doi.org/10.1007/s11432-010-4077-7