Implementation of Linear and Lagrange Interpolation on Compression of Fibrous Peat Soil Prediction

Previous studies have predicted the compression of fibrous peat soils using the Gibson & Lo method. But the prediction process is still done manually so it requires quite a long time. Therefore this research implements linear and Lagrange interpolation methods using Matlab software to speed up the prediction process. This study also carried out a comparison of the results of the implementation of the two methods to determine its effectiveness in making predictions. Based on the results of trials and analysis, it can be seen that the prediction of compression of fibrous peat soil using linear interpolation is more effective than using Lagrange interpolation, this can be proven by the smaller average RMSE prediction results using linear interpolation, with a difference in the average value of RMSE 7.7. Besides, prediction testing using Lagrange interpolation requires longer time, because it still does the iteration process as much as laboratory test data

Numerical Simulation of a Class of Hyperchaotic System Using Barycentric Lagrange Interpolation Collocation Method

Hyperchaotic system, as an important topic, has become an active research subject in nonlinear science. Over the past two decades, hyperchaotic system between nonlinear systems has been extensively studied. Although many kinds of numerical methods of the system have been announced, simple and efficient methods have always been the direction that scholars strive to pursue. Based on this problem, this paper introduces another novel numerical method to solve a class of hyperchaotic system. Barycentric Lagrange interpolation collocation method is given and illustrated with hyperchaotic system (x˙=ax+dz-yz,y˙=xz-by,  0≤t≤T,z˙=cx-z+xy,w˙=cy-w+xz,) as examples. Numerical simulations are used to verify the effectiveness of the present method