By Lewins J. D. (Ed.), Becker M.
The assurance during this sequence levels from the experimental to the theoretical, delivering whatever for all readers devoted to the secure and beneficial improvement of nuclear energy and its linked applied sciences.
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1 Identifiability Since H and are common b e t w e e n and no difference is found in and under the condition that the equivalent feedback loops are the same type of structure, 4 blocks of transfer functions. Under the condition, we can obtain the original open loop transfer function matrices from of a numerical innovation model by using the transformation formula (10): After matrix calculations of closed loop transfer function matrix and from according to 44 Eq. K. KISHIDA (10), we have where and are suitable matrices.
1) in Section II. 2 Non-Markov Effect and Hidden Variable Since the theory of zero power reactor noise is well established, noise phenomena in a zero power reactor are illustrated to understand the formalism mentioned in Section III. 1. The aim of this section is not to introduce the noise theory of a zero power reactor, but to show how a hidden variable affects the output time series data, by setting the precursor as the hidden variable. The two state variables of the theory are the total numbers of neutron and precursor, since the thermal, mechanical, and hydraulic effects are negligible in a zero power reactor.
3. 34 IV. 2 K. KISHIDA Weights of AR Poles  Since an AR model is a linear equation, it is important to examine properties of its eigenvalues and eigenvectors mathematically. If poles of an AR model correspond to eigenvalues, then weights of AR pole become eigenvectors. Since the asymptotic properties of pole location of AR models have been made clear as in Section IV. 1, we will examine properties of weights. In order to proceed analytically, the following properties of an ARMA (d,1) process are assumed to take advantage of the asymptotic pole location rule of AR-type models: (1) All ARMA poles are located inside the convergence circle.