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A3 = bw(1,2 ) (bf (1,2 ) + bf (1,6) ba(1,1) ) A4 = aw(1,2 ) A5 = aw(1,2 ) (af (1,1) - aa(1,1) bf (1,6) ) A6 = bw(1,2 ) bf (1,6) ba(1,2 ) and the denominator coef cients of s are: B1 = aw(1,1) + af (1,1) - aa(1,1) bf (1,6) B2 = aw(1,1) (af (1,1) - aa(1,1) bf (1,6) ) - bw(1,2 ) bf (1,3) Alternatively, if outlet air temperature is of interest, Equations 5.38 5.40 can be re-arranged to give the transfer function set for T : ao A s + A8 Tao (s) = 2 7 uw (s) s + B1s + B2 Tao (s) A9 s 2 + A10 s + A11 = ua (s) s 2 + B1s + B2 Tao (s) A12 = 2 Twi (s) s + B1s + B2 Tao (s) A13 s 2 + A14 s + A15 = Tai (s) s 2 + B1s + B2 for which the numerator coef cients of s are: A7 = aa(1,1) bf (1,1) A8 = aa(1,1) (aw(1,1) bf (1,1) + bw(1,1) bf (1,3) ) A9 = ba(1,1) A10 = aa(1,1) bf (1,2 ) + ba(1,1) (aw(1,1) + af (1,1) ) A = a 11 w(1,1) aa (1,1) bf (1,2 ) + ba (1,1) (aw(1,1) af (1,1) - bw(1,2 ) bf (1,3) ) A12 = aw(1,2 ) aa(1,1) bf (1,3) A13 = ba(1,2 ) A14 = ba(1,2 ) (aw(1,1) + af (1,1) ) A = b 15 a (1,2 ) (aw(1,1) af (1,1) - bw(1,2 ) bf (1,3) ) Figures 5.4(a) & 5.4(b) show the transfer function sets for both T and T expressed in cuswo ao tomary block diagram structures. It should be evident that for situations in which acceptable

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This leads to a classi cation of problems that are P (for polynomial), NP (for non-deterministic polynomial), and NP-complete. A problem falls in the class P if it can be solved at worst in polynomial time (such as with a push-down automaton). A problem falls in the class NP if it can only be solved by a Turing Machine that allows more than one branch with the same label. However, since there are a number of possible exit routes from a node in the automaton for the same character in the string, all of them will need to be tried. Since a Turing Machine can do what it likes with the string, including rewriting it, it will not be known when to stop the machine if it continues to run for a long time. All that can be said is that, if the string is a valid string of the language, the Turing Machine will eventually halt. The reason this class of problems is called non-deterministic polynomial is to express the theoretical view that, if there is an in nite number of machines each of which could explore each path of the search tree in parallel, the problem could be solved in polynomial time. However, some problems are such that the exponential nature of the problem may soon lead to there being more paths to explore than computers on the planet, or even the universe. Finally, the class NP-complete refers to those problems that are NP but where the solution, if returned, can be veri ed in linear or polynomial time at worst. For instance, searching for any route between S and G is

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