TY - GEN
T1 - A generic fuzzy neuron and its application to motion estimation
AU - Kouzani, Abbas Z.
AU - Bouzerdoum, Abdesselam
N1 - Publisher Copyright:
© Springer-Vertag Berlin Heidelberg 1996.
PY - 1996
Y1 - 1996
N2 - The advantages of fuzzy sets and neural networks in emulating the human brain capabilities motivated the development of fuzzy neural networks. Various models of fuzzy neurons have been proposed as the basic element of fuzzy neural networks. In this paper, we introduce a generic fuzzy neuron as an extension of existing fuzzy neuron models. In our model, all the states of activity are given in terms of fuzzy sets with relative grades of membership distributed over the interval [0, 1]. The inputs and outputs are fuzzy sets over different universes of discourse. The connection, aggregation, and activation functions, which determine the operation of the neuron, are fuzzy relations. When the inputs to a function are fuzzy sets over the same universe of discourse, the function can be any fuzzy operation in class of triangular norms or triangular conorms. To evaluate the operation of the fuzzy neuron, a fuzzy neural network architecture based on the generic fuzzy neuron has been developed for motion estimation. The five-layer feed forward fuzzy neural network emulates a fuzzy motion estimation algorithm. Seven simplified versions of fuzzy neurons are defined and utilized in the fuzzy neural network. The results of simulations on thousands of 64×64, 6-bit synthetic image frames containing moving objects under different conditions are reported.
AB - The advantages of fuzzy sets and neural networks in emulating the human brain capabilities motivated the development of fuzzy neural networks. Various models of fuzzy neurons have been proposed as the basic element of fuzzy neural networks. In this paper, we introduce a generic fuzzy neuron as an extension of existing fuzzy neuron models. In our model, all the states of activity are given in terms of fuzzy sets with relative grades of membership distributed over the interval [0, 1]. The inputs and outputs are fuzzy sets over different universes of discourse. The connection, aggregation, and activation functions, which determine the operation of the neuron, are fuzzy relations. When the inputs to a function are fuzzy sets over the same universe of discourse, the function can be any fuzzy operation in class of triangular norms or triangular conorms. To evaluate the operation of the fuzzy neuron, a fuzzy neural network architecture based on the generic fuzzy neuron has been developed for motion estimation. The five-layer feed forward fuzzy neural network emulates a fuzzy motion estimation algorithm. Seven simplified versions of fuzzy neurons are defined and utilized in the fuzzy neural network. The results of simulations on thousands of 64×64, 6-bit synthetic image frames containing moving objects under different conditions are reported.
UR - http://www.scopus.com/inward/record.url?scp=84949844507&partnerID=8YFLogxK
U2 - 10.1007/3-540-61988-7_20
DO - 10.1007/3-540-61988-7_20
M3 - Conference contribution
AN - SCOPUS:84949844507
SN - 9783540619888
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 144
EP - 171
BT - Fuzzy Logic, Neural Networks and Evolutionary Computation - IEEE/Nagoya-University World Wisepersons Workshop, Selected Papers
A2 - Furuhashi, Takeshi
A2 - Uchikawa, Yoshiki
PB - Springer Verlag
T2 - 4th World Wisepersons Workshop on Fuzzy Logic, Neural Networks and Evolutionary Computation, WWW 1995
Y2 - 14 November 1995 through 15 November 1995
ER -