- Journal Home
- Volume 21 - 2024
- Volume 20 - 2023
- Volume 19 - 2022
- Volume 18 - 2021
- Volume 17 - 2020
- Volume 16 - 2019
- Volume 15 - 2018
- Volume 14 - 2017
- Volume 13 - 2016
- Volume 12 - 2015
- Volume 11 - 2014
- Volume 10 - 2013
- Volume 9 - 2012
- Volume 8 - 2011
- Volume 7 - 2010
- Volume 6 - 2009
- Volume 5 - 2008
- Volume 4 - 2007
- Volume 3 - 2006
- Volume 2 - 2005
- Volume 1 - 2004
Cited by
- BibTex
- RIS
- TXT
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.
}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/710.html} }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.