Wei, "Recurrent neural network for computing the Drazin
inverse," IEEE Transactions on Neural Networks and Learning Systems, vol.
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Example 2: Carl Davis buys an
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where X is called the Drain
inverse of A, denoted by X = [A.sup.D], and it is unique.
In this paper, we will use the NIM to solve
inverse heat conduction problems (1)-(6).
Inverse Problem: we want to recover the solution u(r,t) [member of] [L.sup.2]((0,R),[rho]) with 0 [less than or equal to] t < T from the final time data u(r,T) := g(r) [member of] [L.sup.2]((0, R), [rho]) where [L.sup.2]((0, R), [rho]) denotes the Hilbert space of squares Lebesgue measurable functions with weight [rho](r) = r/[R.sup.2] defined on (0,R).
In section 4, Weyl solution, Weyl function are defined and uniqueness theorem for
inverse problem according to Weyl function is proved.
Therefore, the
inverse of the
inverse radial distortion is calculated 10000 times and compared to the original distortion coefficients [9].
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