Convergence of gradient method for training ridge polynomial neural network-Reference-Cited by-同舟云学术

Convergence of gradient method for training ridge polynomial neural network

Published:2012-04-17 Issue:S1 Volume:22 Page:333-339
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Yu Xin,Deng Fei

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-012-0915-4.pdf

Reference16 articles.

1. Giles CL, Maxwell T (1987) Learning, invariance, and generalization in a high-order neural network. Appl Opt 26(23):4972–4978

2. Rumelhart DE, Mcclelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Britian, Cambridge

3. Durbin R, Rumelhart DE (1989) Product units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput 1:133–142

4. Shin Y, Ghosh J (1991) The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation. Neural Netw 1:13–18

5. Shin Y, Ghosh J (1992) Approximation of multivariate functions using ridge polynomial networks. Neural Netw 2:380–385

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The novel characteristics for training Ridge Polynomial neural network based on Lagrange multiplier;Alexandria Engineering Journal;2023-03

2. Convergence of Batch Gradient Method Based on the Entropy Error Function for Feedforward Neural Networks;Neural Processing Letters;2020-10-23

3. WSBP/NWBP small-world neural network and its performance for wind power forecasting;CSEE Journal of Power and Energy Systems;2019-08-01

4. Accelerating SGD using flexible variance reduction on large-scale datasets;Neural Computing and Applications;2019-06-24

5. Particle Swarm Optimization Based Higher Order Neural Network for Classification;Computational Intelligence in Data Mining - Volume 1;2014-12-11