Information Science and Engineering College of Ningbo University, Ningbo, China

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Abstract—As a linear system identification method developed in recent years, Random Subspace Algorithm (SSI) can effectively extract modal parameters from environmental incentive structure. However, this algorithm is not suitable for the embedded system due to its long calculation and low efficiency. For this consideration, improved from the data-based classical stochastic subspace algorithm, this article puts forward the partial projection SSI algorithm with higher efficiency. The basic idea of the improvement is to use the part of the output data rather than all data as a “past” signal, which greatly reduce the calculation and improve the efficiency of the algorithm as the result. Finally the simulation test and actual application of the improved algorithm show that the improved algorithm can achieve experimental results faster, which is still ideal even in strong noise environment. This algorithm improves the calculation efficiency with no loss of accuracy.

Index Terms—Random subspace algorithm, part of the projection, embedded, computational efficiency.

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Cite: Wang Xiao-Dong, Zhou Kai, Ye Qing-Wei, Wu Li-Gang, "The Simulation and Application Research of an Improved SSI Algorithm," Journal of Software vol. 8, no. 9, pp. 2246-2252, 2013.