Open AccessBook
Fundamentals of neural networks
Reads0
Chats0
About:
The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
Citations
More filters
Journal ArticleDOI
Forecasting methodologies for Ganoderma spore concentration using combined statistical approaches and model evaluations
TL;DR: Establishment of a link between occurring symptoms of sensitization to Ganoderma spp.
Journal ArticleDOI
A Neural Network-Based Approach for Statistical Probability Distribution Recognition
Chao-Ton Su,C.-J. Chou +1 more
TL;DR: A neural network-based approach for probability distribution recognition using two types of neural networks, backpropagation and learning vector quantization, is proposed and results demonstrate that the proposed approach outperforms the traditional statistical approach.
Journal ArticleDOI
The EDAM project: Mining atmospheric aerosol datasets
Raghu Ramakrishnan,James J. Schauer,Lei Chen,Zheng Huang,Martin M. Shafer,Deborah S. Gross,David R. Musicant +6 more
TL;DR: An overview of EDAM (Exploratory Data Analysis and Management), a joint project between researchers in Atmospheric Chemistry and Computer Science, is presented, to develop techniques that have broader applicability in atmospheric aerosol analysis.
Journal ArticleDOI
Determination of wind dissipation maps and wind energy potential in Burdur province of Turkey using geographic information system (GIS)
TL;DR: In this paper, the wind speed values of between 2009 and 2016 were obtained from four different stations located in Burdur in order to analyze the wind energy potential of Burdurate Province.
Journal ArticleDOI
Unified knowledge based economy hybrid forecasting
TL;DR: The proposed KBE hybrid forecasting model utilises a 2-stage ANN model which is fed with a panel data set structure and consists of a feed-forward neural network that feeds to a Kohonen's self-organising map (SOM) in the second stage of the model.