Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71584
Title: Recursive percentage based hybrid pattern (RPHP) training for curve fitting
Authors: Uei, G.S. 
Ramanathan, K.
Keywords: Genetic algorithms
Hybrid learning
Pattern Learning
Percentage based training
Task decomposition
Issue Date: 2004
Source: Uei, G.S.,Ramanathan, K. (2004). Recursive percentage based hybrid pattern (RPHP) training for curve fitting. 2004 IEEE Conference on Cybernetics and Intelligent Systems : 445-450. ScholarBank@NUS Repository.
Abstract: In this paper, we present the RPHP training algorithm, which finds several good local optimal points (pseudo global optima) automatically using an efficient combination of global and local search algorithms. This overcomes the problem of supervised learning algorithms being trapped in a local optima. Further, to solve a test pattern, we use a modified version of the Kth nearest neighbor (KNN) algorithm as a second level pattern distributor. We tested our approach on three curve fitting problems, whose coefficients were estimated both using genetic algorithms and the RPHP algorithm. The problems were chosen such that they had a small probability of finding a global optimal solution. It was found that the RPHP algorithms performed faster and improved generalization accuracy by as much as 25%.
Source Title: 2004 IEEE Conference on Cybernetics and Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/71584
ISBN: 0780386442
Appears in Collections:Staff Publications

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