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This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives which employs the flux error for estimating the rotor speed, but overcomes the pure integration problems by using a novel adaptive integration method based on neural adaptive filtering. A linear neuron (the ADALINE) is employed for the estimation of both the rotor speed and the rotor flux-linkage with a recursive total least-squares (TLS) algorithm (the TLS EXIN neuron) for online training. This neural model is also used as a predictor, that is with no feedback loops between the output of the neural network and its input. The proposed scheme has been implemented in a test setup and compared with an MRAS ordinary least-squares speed estimation with low-pass filter integration, with the well-known Schauder's scheme and with the latest Holtz's scheme. The experimental results show that in the high and medium-speed ranges with and without load, the four algorithms give practically the same results, while in low-speed ranges (that is, below 10 rad/s ) the TLS-based algorithm outperforms the other three algorithms. Successful experiments have also been made to verify the robustness of the algorithm to load perturbations and to test its performance at zero-speed operation.