Title :
Equivalence between neural networks and fuzzy systems
Author :
Gaweda, Adam E. ; Zurada, Jacek M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA
Abstract :
Demonstrates that a single-hidden layer feedforward neural network is equivalent to a fuzzy inference system with relational rule antecedents. The method establishes a link between networks weights and fuzzy system parameters and defines the upper bound on the number of fuzzy rules required to represent the network. An application example illustrates the proposed idea
Keywords :
feedforward neural nets; fuzzy logic; fuzzy systems; transfer functions; fuzzy inference system; networks weights; relational rule antecedents; single-hidden layer feedforward neural network; Computer networks; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Merging; Neural networks; Nonlinear systems; Transfer functions; Upper bound;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939555
