A PowerShell Implementation of the popular Bash Screenshot System Information utility (i.e. screenFetch)...
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Updated
Nov 27, 2014 - PowerShell
A PowerShell Implementation of the popular Bash Screenshot System Information utility (i.e. screenFetch)...
PhD thesis: Development of User-Friendly System Identification Techniques
Code that trains cancer soft-robot networks
Unscented estimation and adaptive control package
Code skeletons for implementing PMH in MATLAB based on the repo pmh-tutorial
Hierarchical Bayesian approaches for robust inference in ARX models
NIPS 2017 Paper implementation challenge Learning Linear Dynamic Systems via Spectral Filtering
SON-EM - Algorithm for parameter estimation of hybrid time-varying parameter systems using Sum of Norms regularization and Expectation Maximization
Matlab application and Arduino code to establish a bidirectional Serial Communication for Process Control System Data Acquisition
Estimation of minimal inertial parameters of a space robot for motion planning and control
Moves an arbitrary weight on the end of a motor to a specified height
Constructing Metropolis-Hastings proposals using damped BFGS updates
My collection of implementations of adaptive filters.
Matlab scripts to perform system identification for muti-rotor systems
C++ 11 implementation of neural networks for nonlinear system modeling and control with multiple NARMA-L2 models
A set of Matlab/Octave files that performs a method of Nonlinear System Identification.
Implementation of the algorithm described in the following paper. Korenberg, M., Billings, S.A. and Liu, Y.P. (1987) An Orthogonal Parameter Estimation Algorithm for Nonlinear Stochastic Systems
A collection of Matlab routines for constructing Radial Basis Function (Neural) Network models of NARX-type nonlinear dynamical systems from data. It also includes the possibility of incorporating prior information about the underlying system's steady states in the structure selection of the RBF models.
A collection of Matlab routines for illustrating methods for identifying Radial Basis Function (Neural) Network models for NARX-type nonlinear dynamical systems from data, incorporating prior information about the system's fixed points.
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