ABSTRACT
Point estimate method (PEM) is an efficient approach for stochastic system analysis. For most of PEMs, the precision depends on the number of nodes which is always determined subjectively and empirically. In this work, two more objective PEMs are proposed. One is the direct iterative point estimate method (DIPEM), in which all moments from different nodes are compared until the results converge. The other is the adaptive iterative point estimate method (AIPEM), in which the nonlinear degree of function is deduced, then the number of nodes is determined rationally and the moments are obtained. Several numerical cases are analyzed to verify the proposed methods. Results indicate that the efficiency of existing PEMs is the best, but the results may be inaccurate, especially for higher statistical moments. The precision of DIPEM and AIPEM are always high enough, and AIPEM is more efficient relatively, more practical.
