Abstract
In this paper, an algorithm is proposed for identifying multi-variable systems in state-space form from noisy data, which is suitable for implementation on dedicated microprocessor systems. The proposed algorithm uses the normalized stochastic approximation criterion which reduces the computational complexity and memory requirements. It is shown that the overall performance of the proposed stochastic approximation algorithm when using dedicated microprocessor is superior to the extended least-squares method in terms of memory requirements, execution speed per iteration, and the estimation results.
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