✦ LIBER ✦

Differential Evolution Training Algorithm for Feed-Forward Neural Networks

✍ Scribed by Jarmo Ilonen; Joni-Kristian Kamarainen; Jouni Lampinen

Book ID: 110430513
Publisher: Springer US
Year: 2003
Tongue: English
Weight: 147 KB
Volume: 17
Category: Article
ISSN: 1370-4621
DOI: 10.1023/a:1022995128597

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Training Algorithm with Incomplete Data

Training Algorithm with Incomplete Data for Feed-Forward Neural Networks

✍ Song-Yee Yoon; Soo-Young Lee 📂 Article 📅 1999 🏛 Springer US 🌐 English ⚖ 58 KB

Single-Iteration Training Algorithm for

Single-Iteration Training Algorithm for Multi-Layer Feed-Forward Neural Networks

✍ J. Barhen; R. Cogswell; V. Protopopescu 📂 Article 📅 2000 🏛 Springer US 🌐 English ⚖ 221 KB

A new scheme for training feed-forward n

A new scheme for training feed-forward neural networks

✍ Osama Abdel-Wahhab; M.A. Sid-Ahmed 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 410 KB

In this paper we present a new algorithm, which is orders of magnitude faster than the delta rule, for training feed-forward neural networks. It provides a substantial improvement over the method of Scalero and Tepedelenlioglu (IEEE Trans. Signal Process. 40(1) (1992)) in both training time and nume

Constructing and training feed-forward n

Constructing and training feed-forward neural networks for pattern classification

✍ Xudong Jiang; Alvin Harvey Kam Siew Wah 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 329 KB

Node splitting: A constructive algorithm

Node splitting: A constructive algorithm for feed-forward neural networks

✍ Mike Wynne-Jones 📂 Article 📅 1993 🏛 Springer-Verlag 🌐 English ⚖ 490 KB

Hybrid learning schemes for fast trainin

Hybrid learning schemes for fast training of feed-forward neural networks

✍ Nicolaos B. Karayiannis 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 905 KB

Fast training of feed-forward neural networks became increasingly important as the neural network field moves toward maturity. This paper begins with a review of various criteria proposed for training feed-forward neural networks, which include the frequently used quadratic error criterion, the rela