By Uwe Feist, Mike Dario
This publication illustrates and discusses the weapon which used to be created for the only objective of searching out and destroying enemy tanks, attempting to find them, the Panzerjager and Jagdpanzer of the second one international warfare. This ebook has images, textual content and illustrations.
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Extra resources for Panzerjager in action
5 [Quinlan, 1993a]. , 1992]. , 1988; Botta and Giordana, 1993; Botta and Giordana, 1996], that allows the induction process to be guided by the background knowledge of a domain expert. SMART+ is a complex system capable of learning in First Order Logics as well as in Propositional Calculus (as in the present case), here, we only describe the subset of it that was used in this application. As well as GRDT (see chapter 4), FOIL [Quinlan, 1990] and FOCL [Pazzani and Kibler, 1992], SMART+ uses a general to specific learning strategy.
The approach explained in this as well as in the following two sections is characterized by working off-line on a set of examples that can be the same instances used to induce the approximator. The learning rules applied are variants of gradient descent; they are said to work off-line because the update of the parameters becomes effective after seeing the whole learning set. (Y - y)2 denote the square error exhibited by an approximation y = f* (x) of the function Y = j(x) for a specific input x.
Figure 8a shows the initial approximation of a target function built by CART: the function is approximated by means of a histogram. Figure 8b shows the approximation obtained by using the fuzzy controller produced by converting the tree built by CART into a set of fuzzy rules. At last, figure 8c shows the final approximation of the target signal obtained by refining the fuzzy controller on-line. Learning Function Approximators (a) (b) 41 (c) Figure 8 Approximation of the control function Vz obtained using: (a) CART only; (b) after transforming the tree of CART into a fuzzy Controller; (c) after training the fuzzy controller performing the error gradient descent.