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Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation...
Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an...
The brushless doubly-fed induction generator (BDFIG) cancels brushes on the basis of the doubly-fed induction generator by adding a set of stator and rotor windings to guarantee higher reliability. However this also leads to more complicated coupling and more difficulties of parameter measurement. This paper proposes a new and experiment-based method for BDFIG parameter estimation. With this method,...
Genetic adaptive A-Star searching algorithm for optimizing the running profile of a train in a trip under certain constraints is studied. The train trip profile optimization problem is formulated as a multi-constraints nonlinear optimization problem, and the corresponding use of A-Star searching algorithm is introduced. NSGA-II is employed for the adaptive parameters selection of A-Star searching...
To solve remaining useful life prediction problems of nonlinear and non-stationary process of components, a data-driven approach is presented. The approach constructs a state space model (SSM) to describe degradation evolution process; uses extend Kalman filter to estimate state distribution in SSM and take the Expectation-Maximization (EM) algorithm to update parameters. Based on the measured data,...
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