TY - GEN
T1 - Learning Fuzzy Rule-Based Neural Networks for Function Approximation
AU - Higgins, C. M.
AU - Goodman, R. M.
N1 - Funding Information:
This work was supported in part by the Army Research Office under contract number DAAL03-89-K-0126, and in part by DARPA under contract number AFOSR-90-0199.
Publisher Copyright:
© 1992 IEEE.
PY - 1992
Y1 - 1992
N2 - In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on our previous work with discrete-valued data [3]. The rules learned can then be used in a neural network to predict the function value based upon its dependent variables. An example is shown of learning a control system function.
AB - In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on our previous work with discrete-valued data [3]. The rules learned can then be used in a neural network to predict the function value based upon its dependent variables. An example is shown of learning a control system function.
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U2 - 10.1109/IJCNN.1992.287127
DO - 10.1109/IJCNN.1992.287127
M3 - Conference contribution
AN - SCOPUS:2542478212
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 251
EP - 256
BT - Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1992 International Joint Conference on Neural Networks, IJCNN 1992
Y2 - 7 June 1992 through 11 June 1992
ER -