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Table-based controllers are used for discrete universes, where it is feasible to calculate all combinations of inputs. The relation between all input combinations and their corresponding outputs are then arranged in a table. In cases where there are only two inputs and one output, the controller operates on a two-dimensional look-up table. The two dimensions correspond to the inputs, while the entries in the table correspond to the outputs. Finding a corresponding output involves a simple and fast look-up in the table. Table-based controllers become inefficient for situations with a large number of input and output values.

if x^y - { x^y } z := el { z = x\y }

Figure 3.25 The product of the uncertainties in the position and momentum for an electron in a GaAs single quantum well surrounded by barriers of 100 meV height and with constant mass, as a function of the well width

Using the assignment axiom we calculate that { e = x\y } z := e { z=-x\y } . In particular, Thus, by (10.9),

Whereas analytical calculations of (pz) and (pz2) are tedious, with the numerical formalism and the familiarity built up with finite difference expansions, both quantities follow simply as:

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Mamdani and Assilian produced the first fuzzy controller [Mamdani et al. 1975]. Mamdani-type controllers follow the following simple steps: 1. Identify and name input linguistic variables and define their numerical ranges. 2. Identify and name output linguistic variables and define their numerical ranges. 3. Define a set of fuzzy membership functions for each of the input variables, as well as the output variables. 4. Construct the rule base that represents the control strategy. 5. Perform fuzzification of input values. 6. Perform inferencing to determine firing strengths of activated rules. 7. Defuzzify, using centroid of gravity, to determine the corresponding action to be executed.

{ y ^x } z := x { z = x\y } .

Figure 3.25 plots AzApz for a typical single quantum well, as a function of the well width. The form of the curve is very interesting in that it is a non-monotonic function with a minimum at around lw=100 A. This can be understood intuitively in terms of the spatial coordinate as the well width is reduced from large values, Az, which might be considered as the well width, decreases. However, at the same time the confinement energy increases and at narrow well widths lies just below the top of

Similarly, using the assignment axiom and (10.10),

Figure 3.26 The product of the uncertainties in the position and momentum for an electron in a GaAs single quantum well surrounded by Gao.eAlo.4As barriers, as a function of barrier mass and for various well widths

For the table-based and Mamdani controllers, the output sets are singletons (i.e. a single set), or combinations of singletons where the combinations are achieved through application of the fuzzy set operators. Output sets can, however, also be linear combinations of the inputs. Takagi and Sugeno suggested an approach to allow for such complex output sets, referred to as Takagi-Sugeno fuzzy controllers [Jantzen 1998, Takagi and Sugeno 1985]. In general, the rule structure for TakagiSugeno fuzzy controllers is if f 1 ( A 1 is a1,A2 is 0 2 , . . . A n is a n ) then C = f 2 ( a 1 , a 2 , . . . , a n ) where f1 is a logical function, and f2 is some mathematical function of the inputs; C is the consequent, or output variable being inferred, ai is an antecedent, or input variable, and Ai is a fuzzy set represented by the membership function ^Ai- The complete rule base is defined by K rules. The firing strength of each rule is computed using the min-operator, i.e. ak = min {A i (a i )}

{ x^y } z := y { z = x\y } .

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