Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/42627
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

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