An Architecture of Neural Network for Fuzzy Teaching Inputs

A b s t r a c t A neural network for classifcation problems with linguistic terms is proposed. A fuzzy input is represented as a LR-type fizzy set. A generalized pocket algorithm, called f izzy pocket algorithm, that utilizes .IR-type fuzzy sets operations and defuzziJication method is first applied to train a linear threshold unit ( LTU ). This LTU node will classrfj, as many fuzzy input instances as possible. Afterwards, FV nodes that represent fuzzy vectors will then be generated and expanded by the FVGE learning algorithm to classifi, those input instances that cannot be classified by the LTU node. The network structure is automatically generated. Besides, on-line learning is supplied, and learning speed is fast. One sample problems, called knowledge-based evaluator, is considered to illustrate the working of the proposed method. Also, the experimental results are very encouraging.