Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning

Abstract : The paper considers a novel approach to learning the weight matrix of a fuzzy cognitive map. An overview of the state-of-the-art learning methods is presented with a specific emphasis on methods initially developed for artificial neural networks, and later adapted for FCMs. These have mostly been based on the concept of Hebbian learning. Inspired by the amount of success these methods have faced in the past, the paper proposes a new approach based on the application of the delta rule and the principle of backpropagation, both of which were originally designed for artificial neural networks as well. It is shown by simulation experiments and comparison with the existing approach based on nonlinear Hebbian learning that the proposed approach achieves favourable results, and that these are superior to those of the existing method by several orders of magnitude. Finally, some possible lines of further investigation are suggested.
Document type :
Conference papers
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download

https://hal.inria.fr/hal-01459646
Contributor : Hal Ifip <>
Submitted on : Tuesday, February 7, 2017 - 1:06:35 PM
Last modification on : Wednesday, February 13, 2019 - 2:50:08 PM
Long-term archiving on: Monday, May 8, 2017 - 2:20:47 PM

File

978-3-642-41142-7_55_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Michal Gregor, Peter Groumpos. Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. pp.547-556, ⟨10.1007/978-3-642-41142-7_55⟩. ⟨hal-01459646⟩

Share

Metrics

Record views

99

Files downloads

270