Title :
Convergence of Online Gradient Algorithm with Stochastic Inputs for Pi-Sigma Neural Networks
Author :
Kang, Xidai ; Xiong, Yan ; Zhang, Chao ; Wu, Wei
Author_Institution :
Dept. of Appl. Math., Dalian Univ. of Technol.
Abstract :
An online gradient method is presented and discussed for Pi-Sigma neural networks with stochastic inputs. The error function is proved to be monotone in the training process, and the gradient of the error function tends to zero if the weights sequence is uniformly bounded. Furthermore, after adding a moderate condition, the weights sequence itself is also proved to be convergent
Keywords :
convergence; feedforward neural nets; gradient methods; stochastic processes; Pi-Sigma neural networks; convergence; error function; online gradient algorithm; stochastic inputs; weights sequence; Chaos; Computational efficiency; Computational intelligence; Convergence; Feedforward neural networks; Gradient methods; Mathematics; Neural networks; Polynomials; Stochastic processes; Pi-Sigma neural network; convergence; monotonicity; online gradient algorithm; stochastic input;
Conference_Titel :
Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0703-6
DOI :
10.1109/FOCI.2007.371528
