TY - GEN

T1 - Fuzzy logic, neural networks, and brain-like learning

AU - Roy, Asim

AU - Miranda, Raymond

PY - 1997/12/1

Y1 - 1997/12/1

N2 - Reliable learning algorithms are important for both fuzzy logic and neural networks. Widespread application of fuzzy logic can be significantly enhanced by robust and reliable methods for generating fuzzy logic rules from training data. Fuzzy logic learning must satisfy the principles of learning defined by neural net learning theory. The fundamental design and training issues for both fuzzy logic and neural net systems should be the same. These principles require the algorithm not only to design and train the net in polynomial time, but also to make an explicit attempt to generate the smallest possible net, which implies attempting to generate the smallest set of fuzzy rules to describe the phenomenon. That is, it implies making an explicit attempt to generalize. Generalization and polynomial time complexity are aspects of learning not often mentioned in the fuzzy logic literature. This paper is also an attempt to integrate and unify fuzzy logic and neural network architectures and learning. Since both fuzzy logic and neural networks arise from the same learning experience of humans, they must reside in the same computational structure in the human brain, not in different ones. One of the fundamental ideas of fuzzy systems is to better understand and explain complex decisions in natural, linguistic terms. It is shown here that this can be done using standard neural network architectures such as RBF nets.

AB - Reliable learning algorithms are important for both fuzzy logic and neural networks. Widespread application of fuzzy logic can be significantly enhanced by robust and reliable methods for generating fuzzy logic rules from training data. Fuzzy logic learning must satisfy the principles of learning defined by neural net learning theory. The fundamental design and training issues for both fuzzy logic and neural net systems should be the same. These principles require the algorithm not only to design and train the net in polynomial time, but also to make an explicit attempt to generate the smallest possible net, which implies attempting to generate the smallest set of fuzzy rules to describe the phenomenon. That is, it implies making an explicit attempt to generalize. Generalization and polynomial time complexity are aspects of learning not often mentioned in the fuzzy logic literature. This paper is also an attempt to integrate and unify fuzzy logic and neural network architectures and learning. Since both fuzzy logic and neural networks arise from the same learning experience of humans, they must reside in the same computational structure in the human brain, not in different ones. One of the fundamental ideas of fuzzy systems is to better understand and explain complex decisions in natural, linguistic terms. It is shown here that this can be done using standard neural network architectures such as RBF nets.

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U2 - 10.1109/ICNN.1997.611723

DO - 10.1109/ICNN.1997.611723

M3 - Conference contribution

AN - SCOPUS:0030658512

SN - 0780341228

SN - 9780780341227

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

SP - 522

EP - 527

BT - 1997 IEEE International Conference on Neural Networks, ICNN 1997

T2 - 1997 IEEE International Conference on Neural Networks, ICNN 1997

Y2 - 9 June 1997 through 12 June 1997

ER -