Hybrid approach for identification and modelling of non linear chaotic signals

Ever since independence, India has always been an economy dependent on the agricultural sector. The agricultural sector in turn has been dependent heavily on monsoons and its nature. The more accurately we decipher the predictability of rainfall, the better the agricultural produce and a better performing economy. Time series prediction finds various applications in medicine, stock market, meteorology, geology, astronomy, chemistry, biometrics and robotics also. There are various prediction models which enhance the ability to reduce the after effects of the hazards created by such uncertainty. This paper gives a neuro fuzzy approach to the modeling on weather applications particularly rainfall over a region in presence of chaos if any. In other words a prediction model for the analysis of rainfall is done. In local modeling approaches, the independent models which work on different nonlinear systems and processes are very successful in modeling, identification, and prediction applications. Chaotic time series are therefore used in our analysis. The results thus produced give a meager prediction error which is desirable to get an efficient analogy to create a much better prediction model for chaotic neuro fuzzy or adaptive neural network systems.