Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24691
Title: iJADE WeatherMAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network
Authors: Lee, R
Liu, J
Keywords: Java
Fuzzy neural nets
Geophysics computing
Information filters
Multi-agent systems
Software agents
Time series
Weather forecasting
Issue Date: 2004
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part C, Applications and reviews, 2004, v. 34, no. 3, p. 369-377 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part C, Applications and reviews 
Abstract: Weather forecasting has been one of the most challenging problems around the world for more than half a century. Not only because of its practical value in meteorology, but it is also a typical "unbiased" time series forecasting problem in scientific research. In this paper, we propose an innovative, intelligent multiagent-based environment, namely intelligent Java Agent Development Environment (iJADE), to provide an integrated and intelligent agent-based platform in the e-commerce environment. In addition to the facilities found in contemporary agent development platforms, which focus on the autonomy and mobility of the multiagents, iJADE provides an intelligent layer (known as the "conscious layer") to implement various AI functionalities in order to produce "smart" agents. From an implementation point of view, we introduce a weather forecasting system known as iJADE WeatherMAN - a weather forecasting system that uses fuzzy-neuro-based intelligent agents for automatic weather information gathering and filtering, and for time series weather prediction. Compared with the previous studies on single point sources using a similar network and other networks, such as the radial basis function network, learning vector quantization and the Naïve Bayesian network, our experimental results are very promising. This neural-based rainfall forecasting system is useful and can be used in parallel with traditional forecast methods that are used at the Hong Kong Observatory.
URI: http://hdl.handle.net/10397/24691
ISSN: 1094-6977
DOI: 10.1109/TSMCC.2004.829302
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