Better Performance of Neural Networks using Functional Graph for Weather Forecasting

Neural networks have been in use in numerous meteorological applications including weather forecasting. Neural Networks have been used as an effective method for solving many problems in a wide range of application areas. 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 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 are 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 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.