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Download Steam turbines for STAG combined-cycle power systems by F G Baily; A P Rendine; K E Robbins Affiliation: General PDF

By F G Baily; A P Rendine; K E Robbins Affiliation: General Electric Co., Schenectady, NY (USA)

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Define L(t) = E(~lg,). Clearly (L(t),gt)oStST is a right continuous martingale bounded above and below by e211Jlloo and e-2l1Jlloo respectively. 2 that there exists u E A W such that for all 0 ~ t ~ T 1t = + 1t L(t) = 1 + (u(s), dW(s)). We can rewrite the last equality as L(t) (v(s)L(s), dW(s)), 1 where vet) == u(t)j L(t). 4]) that L(t) = exp (I t(v(s), dW(s)) - 41t IIv(s)lI~dS) . 1 that under 'Yo W == W -1· v(s)ds is a Brownian motion with covariance Q. Therefore -logEexp{ - feW)} = Eti (41 T IIv(s)lI~ds + f (w + i· V(S)dS) ) .

The partially-observed MDP setting has been studied in [BJam), where an information state and dynamic programming equations for the value function on the finite horizon are introduced. Structural results for the value function are due to [FGMar]. Early work in minimax control of stochastic systems includes [BR71], where the connection between stochastic and deterministic descriptions of uncertainty is addressed. In the LQG setting, a connection between risk-sensitive control and Hoo control is established in [GD88].

It can further be shown that policy and value iteration techniques can be used to synthesize an optimal policy. See [Cor97] for details, and for extensions to the partial state observations setting. The nature of the discount factors {3, {3', and {3" can be better understood by considering the small-risk limit, '}'--+ 0, of (39). We obtain the following: h~(i) = min{{3kc(i, u) + {3' {3"" P;j(U)h~+l(j)}, k = 0,.... uEU ~ j (40) 30 Stefano P. Coraluppi, Steven I. Marcus Note that this optimality equation is more general than the risk-neutral dynamic programming equation.

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