Title :
NeuFuz: an intelligent combination of fuzzy logic with neural nets
Author_Institution :
Embedded Syst. Div., Nat. Semicond. Corp., Santa Clara, CA, USA
Abstract :
A novel method is presented to combine neural nets with fuzzy logic. The combined technology, NeuFuz, generates fuzzy logic rules and membership functions by learning the system behavior using input-output data. The generated rules and membership functions are then processed using new fuzzy logic algorithms for defuzzification, rule evaluation and antecedent processing which are developed based on neural network architecture and learning. These fuzzy logic algorithms replace conventional heuristic fuzzy logic algorithms and enable full mapping of neural net to fuzzy logic. Full mapping provides an important key feature of generating fuzzy rules and membership functions to meet a pre-specified accuracy level. NeuFuz also significantly improves performance, reliability, reduces design time and minimizes system cost by optimizing number of rules and membership functions.
Keywords :
fuzzy logic; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); NeuFuz; antecedent processing; defuzzification; fuzzy logic rule generation; fuzzy neural net; membership functions; neural nets; neural network architecture; rule evaluation; system behavior learning; Cost function; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Heuristic algorithms; Inference algorithms; Mathematical model; Neural networks; Neurons; Process design;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714340