An asymmetry subsethood‐based neural fuzzy network
Cheng‐Jian Lin, Tzu‐Chao Lin, Chi‐Yung Lee · Asian Journal of Control · 2008
Abstract This paper proposes a novel asymmetric subsethood‐based neural fuzzy network (ASNFN) that identifies and controls nonlinear dynamic systems. ASNFN has the flexibility to handle both numeric and linguistic inputs. The numeric inputs in ASNFN are fuzzified by input nodes as tunable feature fuzzifiers. Connections in ASNFN are represented by pseudo‐Gaussian fuzzy sets which provide the neural fuzzy network with higher flexibility and attain more accurate optimization. An online self‐constructing learning algorithm that is constructed and implemented in ASNFN consists of structural learning and parametric learning, and would create adaptive fuzzy logic rules. Computer simulations illustrate the performance and capability of the proposed model in identifying a dynamic system, in Iris data classification, and in approximating a nonlinear function. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society