The problem of identifying discrete time affine hybrid systems with noisy measurements is addressed in this paper. Given a finite number of measurements of input/output and a bound on the measurement noise, the objective is to identify a switching sequence and a set of affine models that are compatible with the a priori information, while minimizing the number of affine models. While this problem has been successfully addressed in the literature if the input/output data is noise-free or corrupted by process noise, results for the case of measurement noise are limited, e.g., a randomized algorithm has been proposed in a previous paper . In this paper, we develop a deterministic approach. Namely, by recasting the identification problem as polynomial optimization, we develop deterministic algorithms, in which the inherent sparse structure is exploited. A finite dimensional semi-definite problem is then given which is equivalent to the identification problem. Moreover, to address computational complexity issues, an equivalent rank minimization problem subject to deterministic LMI constraints is provided, as efficient convex relaxations for rank minimization are available in the literature. Numerical examples are provided, illustrating the effectiveness of the algorithms.