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Arctic Gold (Stephen Coonts' Deep Black, Book 7) by Stephen Coonts, William H. Keith Jr.

By Stephen Coonts, William H. Keith Jr.

Within the Arctic, American intelligence operatives are abducted whereas investigating Russian submarines—a consistent, covert presence underneath the ice caps. In Washington, ex-Marine Charlie Dean and his group at table 3 hint the kidnapping again to the Russian mafiya, who've their points of interest set at the large reserves of oil that lie millions of ft under the ocean’s floor.

While Dean is distributed to the Arctic to rescue the hostages, the gorgeous Lia DeFrancesca penetrates a seriously guarded dacha at the shorelines of the Black Sea. the following she learns the explosive fact approximately Russia and its Arctic oil—one that may rate Dean and his Deep Black staff their lives…and force the world’s superpowers to the edge of warfare.

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Extra resources for Arctic Gold (Stephen Coonts' Deep Black, Book 7)

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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.

E. it is replaced by: r;(x) = e- (L:7=1 (~) 2) Here, kernels have a hyperelliptical rather than a a hyperspherical shape. t. the approximation task. As a consequence, although they do not add any expressive power to the formalism, they allow to approximate the target function by means of less hidden neurons without any loss of accuracy. Less neurons mean a faster training phase. 2 A fuzzy controller architecture Fuzzy Controllers are universal function approximators developed within the fuzzy logic community [Zadeh, 1992; Berenji, 1992].

Aln gi .. 1) where r is the reinforcement, bij is a reinforcement baseline and gi is a probability function for the output of a stochastic unit as a function of the weights i on which the output depends. This means that (1' - bij) is an unbiased estimate for the derivative. lwij = a(r - bij)--"l--' UWij 8{};3 For the following choice of stochastic units: 1 /I. 4) Learning sensor assisted assembly operations 51 The RL architecture features two neural nets with 12 inputs (position and force) and 6 outputs.

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