Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing

• Published in: Neurocomputing (Amsterdam) 2001-02, Vol.36 (1), p.29-44
• Main Author: Lingras, Pawan
• Format: Article
• Language: English
• Subjects: Fuzzy computing; Interval calculus; Neural networks; Rough computing
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/S0925-2312(00)00334-9

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.