Competitive/comparative learning

The cost efficiency of FCEVs relative to their competitors depends on the vehicle ownership costs as well as the fuel cost per kilometer. It can be assumed that FCEVs at some time in the future can be produced at costs comparable to the costs of similar ICVs, HEVs or PHEVs. It is difficult to predict the time required for the necessary research breakthroughs to emerge and for the necessary industrial learning to take place. If it is further assumed that FCEV production at comparable costs will be possible at some time in 2015-25, the question of competitiveness becomes a matter of fuel cost per kilometer at that time. 

In the case of competitive learning, the distance between the input vector, X, is compared to the weight vector, w, by using an appropriate distance measure. Usually, the Euchdian distance is applied (c Eq. (5.87)). In detail, the following steps are followed ... 

I LEARNING CHECK 10.4 Compare a noncompetitive and a competitive inhibitor with regard to the following ... 

This reaction has been studied several times in rf ion traps with increasing accuracy. The results are summarized in Fig. 3.22 (see also Refs. 15 and 55). For P-H2 the rate coefHcient for radiative association is 1.7 x 10 cm s at 10 K, while the value for n-H2 is 2.5 times smaller. As discussed in detail in Ref. 22, much more has been learned about such processes, e.g. the competition between complex life time and radiative decay, by comparing ternary and radiative association and by isotopic substitution. 

The number of cycles after full implicit learning has to be determined experiment tally and compared to the various predictions of the competitive hypotheses. 

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