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A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model
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DOI：10.1016/j.neucom.2013.01.024.png)
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Abstract
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In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Flexible Beta Basis Function Neural Tree Model
Extended genetic programming
Opposite-based particle swarm optimization algorithm
Time-series forecasting
Control system
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