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Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 20, 2014 - Issue 4
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Original Articles

Dimension reduction for second-order systems by general orthogonal polynomials

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Pages 414-432 | Received 18 Jan 2013, Accepted 15 Nov 2013, Published online: 12 Dec 2013

Figures & data

Table 1. Differential recurrence coefficients for classical orthogonal polynomials.

Figure 1. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 1, the BTR method and the Arnoldi method in Example 1.

Figure 1. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 1, the BTR method and the Arnoldi method in Example 1.

Figure 2. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 1.

Figure 2. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 1.

Table 2. Computational times of obtaining reduced models for Example 1.

Figure 3. Transient responses (a) and relative errors (b) of the output y3(t) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 2.

Figure 3. Transient responses (a) and relative errors (b) of the output y3(t) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 2.

Table 3. Computational times of obtaining reduced models for Example 2.

Figure 4. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 3.

Figure 4. Transient responses (a) and relative errors (b) of the reduced models obtained by Algorithm 2, the SOBT method and the SOAR method in Example 3.

Table 4. Computational times of obtaining reduced models for Example 3.

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