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This paper deals with a new experimental approach to the parameter estimation of induction motors with least-squares techniques. In particular, it exploits the robustness of total least-squares (TLS) techniques in noisy environments by using a new neuron, the TLS EXIN, which is easily implemented online. After showing that ordinary least-squares (OLS) algorithms, classically employed in the literature, are quite unreliable in the presence of noisy measurements, which is not the case for TLS, the TLS EXIN neuron is applied numerically and experimentally for retrieving the parameters of an induction motor by means of a test bench. Additionally, for the case of very noisy data, a refinement of the TLS estimation has been obtained by the application of a constrained optimization algorithm which explicitly takes into account the relationships among the K-parameters. The strength of this approach and the enhancement obtained is fully demonstrated first numerically and then verified experimentally.