system may be used to provide for a parameterized nonlinear function that fits the data by using its basic interpolation capabilities. For instance suppose that we have a human expert who controls some process and we observe how she or he does this by observing what numerical plant input the expert picks for the given numerical data that she or he observes Suppose further that we have many such associations between decision-making data "The methods in this chapter will show how to construct rules for a fuzzy controller from this data(i.e, identify a controller from the human-generated decision-making data) and in this sense they provide another method to design controllers Yet another problem that can be solved with the methods in this hapter 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 fuzz estimator, For some plants it may be possible to perform this procedure with actual experimental data(by physically adjusting the parameter to besystem may be used to provide for a parameterized nonlinear function that fits the data by using its basic interpolation capabilities. For instance, suppose that we have a human expert who controls some process and we observe how she or he does this by observing what numerical plant input the expert picks for the given numerical data that she or he observes. Suppose further that we have many such associations between "decision-making data." The methods in this chapter will show how to construct rules for a fuzzy controller from this data (i.e., identify a controller from the human-generated decision-making data), and in this sense they provide another method to design controllers. 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, Forsome plants it may be possible to perform this procedure with actual experimental data (by physically adjusting the parameter to be