{"type":"ScholarlyArticle","title":"An Effective Learning Method for Max-Min Neural Networks","authors":[{"name":"Loo-Nin Teow","slug":"loo-nin-teow","given":"Loo-Nin","family":"Teow","affiliation":""},{"name":"Kia-Fock Loe","slug":"kia-fock-loe","given":"Kia-Fock","family":"Loe","affiliation":""}],"abstract":"Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-v<~.lued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiable, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max-min error functions. This method is applied to a \"typical\" fuzzy-neural network model employing max-min activation functions. We show how this network can be trained to perform function approximation; its performance was found to be better than that of a conventional feedforward neural network.","doi":null,"url":"https://ijcai.org/proceedings/1997/164","pdf":"https://ijcai.org/proceedings/1997/0164.pdf","markdown":"https://ijcai.org/proceedings/1997/164.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/164","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Neural Networks","track":"Neural Networks","number":164,"pages":{"first":1134,"last":1138},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}