A rough set based fuzzy neural network algorithm for weather prediction

A rough set based fuzzy neural network algorithm is proposed to solve weather prediction. In order to avoid arriving at local minimum value, the least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain global convergence. Because structure of fuzzy neural networks, the numbers of rules and the initial weights are difficult to be determined, here the rough sets method is introduced to decide the numbers of rules and original weights. Finally, the proposed algorithm through standard data set is testified to have better rationality and availability than BP algorithm. Experiment results show the algorithm for weather prediction may get better effect.