An improved optimisation framework for fuzzy time-series prediction

This paper presents a hybrid identification method for Takagi-Sugeno-Kang (TSK) fuzzy model by means of a combination of optimisation techniques. First, the K-means clustering algorithm is used to get information granules (centres of clusters) which are used as the initial location of apexes of the MFs in the premise and the prototypes of the polynomial functions used in the consequent parts of the fuzzy rules. Subsequently, the initial fuzzy system is evolved iteratively by means of a hybrid learning. In particular, the premise part parameters are tuned by a combination of a Island Model Parallel Genetic Algorithm (IMPGA) and a space search Memetic Algorithm (MA) while the consequent parameters of the system are derived optimally by an improved QR Householder least square method (LSM). The optimisation search algorithm (IMPGA+MA) allows exploring the search space in multiple trajectories simultaneously to avoid getting trapped in local-optimal while the improved LSM helps minimize the occurrences of the underflow and overflow problems when dealing with floating point numbers. The proposed optimisation framework can be applied for a variety of application areas such as function approximation, time-series prediction, etc. However, in this paper, the proposed method is only evaluated using the well-known Mackey-Glass time-series prediction benchmark and has shown a better prediction accuracy than any other previous works of the same problem.