Local Approximation Improvement of Trajectory Piecewise Linear Macromodels through Chebyshev Interpolating Polynomials

• Main Authors: Farooq, Muhammad Umer, Xia, Likun, Hussin, Fawnizu Azmadi, Malik, Aamir Saeed
• Format: Conference or Workshop Item
• Published: 2013
• Subjects: QA Mathematics
• Online Access: http://eprints.utp.edu.my/10916/1/ASP-DAC%202013.pdf; http://www.aspdac.com/aspdac2013/; http://eprints.utp.edu.my/10916/

We introduce the concept of two dimensional (2D) scalability of trajectory piecewise linear (TPWL) through the exploitation of Chebyshev interpolating polynomials in each piecewise region. The goal of 2D scalability is to improve the local approximation properties of TPWL macromodels. Horizontal scalability is achieved through the reduction of number of linearization points along the trajectory; vertical scalability is obtained by extending the scope of macromodel to predict the response of a nonlinear system for inputs far from training trajectory. In this way more efficient macromodels are obtained in terms of simulation speed up of complex nonlinear systems. The methodology developed is to predict the nonlinear responses generated by faults introduced in Micro Electro-Mechanical Systems (MEMS) accelerometer during fabrication, that are used to obtain the seismic images for oil and gas discovery. We provide the implementation details and illustrate the 2D scalability concept with an example using nonlinear transmission line.