Neo-Fuzzy Integrated Adaptive Decayed Brain Emotional Learning Network for Online Time Series Prediction

Abstract

Adaptive decayed brain emotional learning (ADBEL) network is recently proposed for the online time series forecasting problems. As opposed to other popular learning networks, such as multilayer perceptron, adaptive neuro-fuzzy inference system, and locally linear neuro-fuzzy model, ADBEL network offers lower computational complexity and fast learning, which make it an ideal candidate for the time series prediction in an online fashion. In fact, these prominent features are inherited from the mechanism employed by the limbic system of the mammalian brain in processing the external stimuli, which also forms the basis of the ADBEL network. This paper aims at further enhancing the forecasting performance of the ADBEL network through its integration with a neo-fuzzy network. The selection of the neo-fuzzy network is made as it offers features required for online prediction in real time environments including simplicity, transparency, accuracy, and lower computational complexity. Furthermore, this integration is only considered in the orbitofrontal cortex section of the ADBEL network and only three membership functions are employed to realize the neo-fuzzy neuron. Thus, the resultant neo-fuzzy integrated ADBEL (NF-ADBEL) network is still simple and can be deployed in online prediction problems. Few chaotic time series namely the Mackey glass, Lorenz, Rossler, and the Disturbance storm time index as well as the Narendra dynamic plant identification problem are used to evaluate the performance of the proposed NF-ADBEL network in terms of the root mean squared error and correlation coefficient criterions using MATLAB®programming environment.