✦ LIBER ✦

Artificial neural networks in real-time car detection and tracking applications

✍ Scribed by Christian Goerick; Detlev Noll; Martin Werner

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 603 KB
Volume: 17
Category: Article
ISSN: 0167-8655
DOI: 10.1016/0167-8655(95)00129-8

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Real time track identification with arti

Real time track identification with artificial neural networks

✍ G. Athanasiu; P. Pavlopoulos; S. Vlachos 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 484 KB

Development of artificial neural network

Development of artificial neural networks for real time, in situ ellipsometry data reduction

✍ Frank K. Urban III; Dong C. Park; Milad F. Tabet 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 672 KB

Uncertainties in real-time flood forecas

Uncertainties in real-time flood forecasting with neural networks

✍ Dawei Han; Terence Kwong; Simon Li 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 115 KB

## Abstract Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper descr

On-Line Detection of Contamination in a

On-Line Detection of Contamination in a Bioprocess Using Artificial Neural Networks

✍ U. Kr. Bhowmik; G. Saha; A. Barua; S. Sinha 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 373 KB 👁 2 views

Fault detection and classification techn

Fault detection and classification technique in EHV transmission lines based on artificial neural networks

✍ Tahar Bouthiba 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 449 KB

Stabilizing and robustifying the learnin

Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications

✍ M. Onder Efe; Okyay Kaynak 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 918 KB

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. The stability in this context corresponds