Abstract
Analog circuit fault diagnosis can be modeled as a pattern recognition problem. Fault patterns are complicated which has high demands for classification accuracy and efficiency. Therefore a new analog circuit fault diagnosis method using Echo State Networks (ESNs) is proposed. We adopt the time windows function to construct reservoir with corresponding clusters of ESNs inspired by complex network topologies imitating cortical networks of the mammalian brain. Multiple-cluster reservoir is generated instead of non-clustering reservoir of the original ESNs with random sparse connections. We use the number of classes to determine the number of clusters to improve performances in specific analog circuit fault diagnosis problems. Simulation results show the effectiveness of the proposed method.
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Peng, X., Guo, J., Lei, M., Peng, Y. (2011). Analog Circuit Fault Diagnosis with Echo State Networks Based on Corresponding Clusters. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_51
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DOI: https://doi.org/10.1007/978-3-642-21105-8_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21104-1
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