Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/46916
Title: A study of extended minimal resource allocation network (EMRAN) alogrithm for multi-category classification problems
Authors: P. Karuppasami
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2011
Abstract: This dissertation presents a Neural Network (NN) for multi-category classification problems and also presents the use of sequential learning algorithm, Extended Minimal Resource Allocating Network (EMRAN), to approximate the functional relationship between feature vector and class label. EMRAN uses Radial Basis Function (RBF) as its basic component. It has got growing and pruning strategy to find optimal hidden neurons. Its learning is based on Extended Kalman Filter (EKF) algorithm. In order to reduce the computational complexity, it updates only the winner neuron parameters.
Description: 67 p.
URI: http://hdl.handle.net/10356/46916
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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