Sentence examples for mode transition probability from inspiring English sources

The mode transition probability is chosen to be p0=p1=0.8.

Nevertheless, it suffers from a deficiency originated in expensive computation of the time-varying mode transition probabilities.

Due to the limitation of the standard IMM algorithm in real applications, the adaptive fuzzy IMM filter (AFIMMF) proposed in[12] defines several basis sub-models and time-varying mode transition probabilities to reduce its computational complexity.

In order to compare the performance of different multiple model particle filters, and different mode transition probabilities in a controlled manner, a Monte-Carlo simulation based on synthetic data is presented in this section.

This paper studies the H∞ filtering problem for continuous Markov jump linear systems (MJLSs) with partly accessible Markov modes and transition probabilities.

The eigenvector corresponding to the first relaxation mode of the selected transition probability matrix is depicted in Fig. 6A.

Based on formula (4), we construct the transition probability matrices for mode element pairs, each of which is defined as two elements with the same position of two adjacent blocks along horizontal or vertical directions.

The complex networks consist of m modes which switch from one mode to another according to a Markovian chain with known transition probability.

Across the whole posterior probability distribution, for the three state analysis, the mode transition rate out of monogamy was zero, while the transitions from harem-polygyny to monogamy and back to polygynandrous mating were zero for 18% and 41% of the time respectively (Fig. 1B).

In accordance with the transition probability matrix in (28), a possible time sequence of the mode jumps is illustrated in Figure 7. Figure 1 The error state response of (pmb{{e_{1i}}(t)}) ( (pmb{i = 1,2}) ) with out control (pmb{u(t)}).

This mode transition can easily be seen in Figure 7 where the on-road mode probabilities are shown.