The estimate for approximation error of neural network with two weights.
The neural network with two weights is constructed and its approximation ability to any continuous functions is proved. For this neural network, the activation function is not confined to the odd functions. We prove that it can limitlessly approach any continuous function from limited close subset of R(m) to R(n) and any continuous function, which has limit at infinite place, from limitless close subset of R(m) to R(n). This extends the nonlinear approximation ability of traditional BP neural network and RBF neural network.
Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, China.
Fanzi
F
Zeng
Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, China.
Yuting
Y
Tang
D000465:Algorithms; D016571:Neural Networks, Computer