 In the second approach, keeping the data fixed but making successive refinements in nonlinear models is also not guaranteed to lead to any convergence between model behavior and target system behavior. With the loss of linear superposition, any small changes in the model can lead to non-proportional changes in model behavior again rendering the convergence strategy ineffective as a means for confirming the model. Even if a small refinement to the model is made “in the right direction,” there is no guarantee that the nonlinear model will monotonically improve in capturing the target system’s behavior. The small refinement in the model may very well lead to the model behavior diverging away from the system’s behavior. 