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This paper analyzes optimal experiment design for Hammerstein models. We show that, under general conditions, open loop experiments are optimal when one constrains the system input, and also for a specific class of systems when there is a constraint on the output power. Furthermore, we analyze an experiment which uses a binary signal, and show it to be optimal when the system is in open loop. Both results use a strong notion of optimality and expressions for estimation accuracy which are non-asymptotic in model order and asymptotic in data length. We also develop a procedure to design an experiment which uses binary input signals based on semi-definite programming.