Title :
Fuzzy function approximation
Author_Institution :
Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
An additive fuzzy system can approximate any continuous function on a compact domain to any degree of accuracy. Fuzzy systems are dense in the space of continuous functions. The fuzzy system approximates the function by covering its graph with fuzzy patches in the input-output state space. Each fuzzy rule defines a fuzzy patch and connects common-sense knowledge with state-space geometry. Neural or statistical clustering algorithms can approximate the unknown fuzzy patches and generate fuzzy systems from training data
Keywords :
function approximation; fuzzy set theory; neural nets; additive fuzzy system; common-sense knowledge; compact domain; continuous function; fuzzy function approximation; fuzzy patches; fuzzy rule; graph covering; input-output state space; neural algorithms; state-space geometry; statistical clustering algorithms; training data; Clustering algorithms; Costs; Function approximation; Fuzzy sets; Fuzzy systems; Geometry; Hypercubes; Image processing; Signal processing; State-space methods;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.287134
