By Juan Manuel Górriz, Carlos G. Puntonet, J. J. G. de la Rosa, Moisés Salmerón (auth.), Ajith Abraham, Katrin Franke, Mario Köppen (eds.)
This quantity contains the complaints of the 3rd overseas convention on clever structures layout and functions (ISDA 2003) held in Tulsa, united states, August 10-13. present examine in all parts of computational intelligence is gifted together with layout of synthetic neural networks, fuzzy platforms, evolutionary algorithms, hybrid computing platforms, clever brokers, and their purposes in technological know-how, know-how, company and advertisement. major subject matters addressed through the convention are the architectures of clever structures, photo, speech and sign processing, net modeling, info mining, enterprise and administration functions, keep an eye on and automation, software program brokers and data management.
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Additional info for Intelligent Systems Design and Applications
Neural networks generally provide improved performance with the normalized data. The use of original data as input to the neural network may cause a convergence problem. All the weather data sets were, therefore, transformed into values between -1 and 1 through dividing the difference of actual and minimum values by the difference of maximum and minimum values. The main goal of normalization, in combination with weight initialization, is to allow the squashed activity function to work at least at the beginning of the learning phase.
Khan, and A. Abraham simulations of the atmosphere, and is often referred to as computer modeling. Because of the grid coarseness, the dynamical approach is only useful for modeling large-scale weather phenomena and may not predict short-term weather efficiently. Most weather prediction systems use a combination of empirical and dynamical techniques. However, a little attention has been paid to the use of artificial neural networks (ANNs) in weather forecasting (Moro et al. 1994; Kuligowski and Barros 1998; Maqsood et al.
The RBFN network can gave the overall best results in terms of accuracy and training time. The RBFN is better correlated compared to the MLPN, ERNN and HFM. The proposed RBFN network can also overcome several limitations of the MLPN and ERNN such as highly nonlinear weight update and the slow convergence rate. Since the RBFN has natural unsupervised learning characteristics and a modular network structure, these properties make it more effective for fast and robust weather forecasting. Hopfield model shows higher values for RMSE, MAD and MAP.