Abstract
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 practical applications. A rough neuron consists of an upper and a lower neuron. Rough neurons can be used to effectively represent an interval or a set of values. The rough neural networks provide more flexible architectures than exclusive interval-based neural networks. Neofuzzy neurons are used to elaborate on a value by partitioning the crisp value into fuzzy segments for processing 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 augment 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 the architectures of fuzzy-rough and rough-fuzzy subnets. A discussion on potential applications of the subnets is also provided along with examples.
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