Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing

Rough and neo-fuzzy neurons are two different ways of introducing semantic structures in a neural network. Both have been shown to be useful in practi cal applications. A rough neuron consists of an upper and a lower neuron. R ough neurons can be used to effectively represent an interval or a set of v alues. The rough neural networks provide more flexible architectures than e xclusive interval-based neural networks. Neofuzzy neurons are used to elabo rate on a value by partitioning the crisp value into fuzzy segments for pro cessing by a neural network. Previous work has shown that fuzzy values can be used to describe the difference in output of a rough neuron. This paper provides a more comprehensive introduction to serial combinations of rough and neofuzzy neurons in neurocomputing. The neofuzzy neurons are used to au gment output of a rough neuron. On the other hand, rough neurons are shown to be useful for extending the expressive powers of a neofuzzy neuron. The first type of serial combination is termed a rough-fuzzy subnet. The latter serial combination is called a fuzzy-rough subnet. This paper describes th e architectures of fuzzy-rough and rough-fuzzy subnets. A discussion on pot ential applications of the subnets is also provided along with examples. (C ) 2001 Elsevier Science B.V. All rights reserved.