Yet another problem that can be solved with the methods in this chapter is that of how to construct a fuzzy system that will serve as a parameter estimator. To do this, we need data that shows, roughly how the input-output mapping of the estimator should behave (i.e, how it should estimate). One way to generate this data is to begin by establishing a simulation test bed for the plant for which parameter estimation must be performed. Then a set of simulations can be conducted, each with a different value for the parameter to be estimated. by coupling the test conditions and simulation-generated data with the parameter values, you can gather appropriate data pairs that allow for the construction of a fuzzy estimator, For some plants it may be possible to perform this procedure with actual experimental data(by physically adjusting the parameter to be estimated). In a similar way, you could construct fuzzy predictors using the approaches developed in this chapter8 ◼ Yet another problem that can be solved with the methods in this chapter is that of how to construct a fuzzy system that will serve as a parameter estimator. To do this, we need data that shows, roughly how the input-output mapping of the estimator should behave (i.e., how it should estimate). One way to generate this data is to begin by establishing a simulation test bed for the plant for which parameter estimation must be performed. Then a set of simulations can be conducted, each with a different value for the parameter to be estimated .by coupling the test conditions and simulation-generated data with the parameter values, you can gather appropriate data pairs that allow for the construction of a fuzzy estimator, For some plants it may be possible to perform this procedure with actual experimental data (by physically adjusting the parameter to be estimated). In a similar way, you could construct' fuzzy predictors using the approaches developed in this chapter