A learning algorithm of fuzzy neural networks with triangular fuzzy weights
H Ishibuchi, K Kwon, H Tanaka�- Fuzzy sets and systems, 1995 - Elsevier
In this paper, first we propose an architecture of fuzzy neural networks with triangular fuzzy
weights. The proposed fuzzy neural network can handle fuzzy input vectors as well as real
input vectors. In both cases, outputs from the fuzzy neural network are fuzzy vectors. The
input-output relation of each unit of the fuzzy neural network is defined by the extension
principle of Zadeh. Next we define a cost function for the level sets (ie, α-cuts) of fuzzy
outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for�…
weights. The proposed fuzzy neural network can handle fuzzy input vectors as well as real
input vectors. In both cases, outputs from the fuzzy neural network are fuzzy vectors. The
input-output relation of each unit of the fuzzy neural network is defined by the extension
principle of Zadeh. Next we define a cost function for the level sets (ie, α-cuts) of fuzzy
outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for�…