TY - JOUR
T1 - Numerical Dynamic Modeling and Data Driven Control via Least Square Techniques and Hebbian Learning Algorithm
AU - X. Liu & Y. Zhang
JO - International Journal of Numerical Analysis and Modeling
VL - 1
SP - 66
EP - 86
PY - 2010
DA - 2010/07
SN - 7
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/ijnam/710.html
KW - Least Square Learning, fuzzy cognitive map, Takagi Sugeno model, complex dynamic system, Hebbian learning algorithm.
AB - <p style="text-align: justify;">The modelling and controlling for complex dynamic systems which
are too complicated to establish conventionally mathematical mechanism models
require new methodology that can utilize the existing knowledge, human
experience and historical data. Fuzzy cognitive maps (FCMs) are a very convenient,
simple, and powerful tool for simulation and analysis of dynamic systems.
Since human experts are subjective and can handle only relatively simple
FCMs, there is an urgent need to develop methods for automated generation
of FCM models using historical data. In this paper, a novel FCM, which is
automatically generated from data and can be applied to on-line control, is developed
by improving its constitution, introducing Least Square methods and
using Hebbian Learning techniques. As an illustrative example, the simulations
results of truck backer-upper control problem quantifies the performance
of the proposed constructions of FCM and emphasizes its effectiveness and
advantageous characteristics of the learning techniques and control ability.</p>