A growing emphasis on the analysis of time-varying systems has intensified the need for simpler and more efficient identification methods for these systems. In this contribution, we examine the time-varying Hammerstein structure, comprising a memoryless nonlinearity with time-varying parameters followed by a time-varying linear filter. Two existing approaches for the identification of these systems, ensemble approaches and basis expansion methods, are combined as a single algorithm to give a much improved estimation tool. The proposed algorithm, applied to data from a simulation of a time-varying Hammerstein system, is used to construct models of the reflex contribution to joint stiffness and the results obtained are compared to those using the basis expansion method alone.