Bidirectional bridge between neural networks and linguistic knowledge: linguistic rule extraction and learning from linguistic rules

The aim of the paper is to clearly demonstrate that the relation between neural networks and linguistic knowledge is bidirectional. First we show how neural networks can be trained by linguistic knowledge, which is represented by a set of fuzzy rules. Next we show how linguistic knowledge can be extracted from neural networks. Then we discuss the design of classification systems when numerical data and linguistic knowledge are available. Since the relation between neural networks and linguistic knowledge is bidirectional, we can simultaneously utilize these two kinds of information for designing classification systems. For example, neural network-based classification systems can be trained by numerical data and linguistic knowledge. Fuzzy rule-based classification systems can be designed by linguistic knowledge and fuzzy rules extracted from neural networks. The performance of these classification systems is examined by computer simulations.