Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
A Functional Graph Approach for Alarm Filtering and Fault Recovery for Automated Production Systems
WODES '02 Proceedings of the Sixth International Workshop on Discrete Event Systems (WODES'02)
Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Faulty-tolerant algorithm for mapping a complete binary tree in an IEH
WSEAS Transactions on Computers
Construction of virtual backbone on growth-bounded graph with variable transmission range
WSEAS Transactions on Computers
A novel construction of connectivity graphs for clustering and visualization
WSEAS Transactions on Computers
Functional graph model of a neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural architectures optimization and genetic algorithms
WSEAS Transactions on Computers
The continuous hopfield networks (CHN) for the placement of the electronic circuits problem
WSEAS Transactions on Computers
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Multilayered feed forward neural networks possess a number of properties which make them particularly suited to complex problems. Neural networks have been in use in numerous meteorological applications including weather forecasting. As Neural Networks are being more and more widely used in recent years, the need for their more formal definition becomes increasingly apparent. This paper presents a novel architecture of neural network models using the functional graph. The neural network creates a graph representation by dynamically allocating nodes to code local form attributes and establishing arcs to link them. The application of functional graph in the architecture of Electronic neural network and Opto-electronic neural network is detailed with experimental results. Learning is defined in terms of functional graph. The proposed architectures are applied in weather forecasting and X-OR problem. The weather forecasting has been carried out based on various factors consolidated from meteorological experts and documents. The inputs are temperature, air pressure, humidity, cloudiness, precipitation, wind direction, wind speed, etc., and outputs are heavy rain, moderate rain and no rain. The percentage of correctness of the weather forecasting of the conventional neural network models, functional graph based neural network models and the meteorological experts are compared.