Please use this identifier to cite or link to this item:
|Title:||POPFNN-CRI(S) : a fuzzy neural network based on the compositional rule of inference||Authors:||Ang, Kai Keng||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||1998||Abstract:||Fuzzy Neural Networks are hybrid systems that combine the human inference style and natural language description of fuzzy systems with the learning and parallel processing of neural networks. A novel FNN architecture, an improved selforganizing neural network algorithm and two novel fuzzy membership function identification algorithms are proposed. They are the Pseudo Outer-Product based Fuzzy Neural Network using the Compositional Rule of Inference and a Singleton Fuzzifier (POPFNN-CRI(S)), the Modified Learning Vector Quantization (MLVQ) algorithm, the Fuzzy Kohonen Partition (FKP) and the Pseudo Fuzzy Kohonen Partition (PFKP) algorithms.||URI:||http://hdl.handle.net/10356/42627||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SAS Theses|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.