Meta-cognitive fuzzy extreme learning machine

In this paper, a fast learning methodology for neuro-fuzzy inference system (NFIS) referred to as meta-cognitive fuzzy extreme learning machine (McFELM) is proposed. It is based on the original OS-Fuzzy-ELM algorithm incorporating principles of human meta-cognition to make the learning more effective. McFELM has two components: the cognitive component and the meta-cognitive component. The cognitive component is a fuzzy extreme learning machine which learn sequential data in a one-by-one mode or a chunk-by-chunk mode with fixed or varying chunk size, while the meta-cognitive component controls the learning process of the cognitive component using a self-regulating mechanism to decide what-to-learn, when-to-learn, and how-to-learn. Unlike the OS-Fuzzy-ELM algorithm which uses all arriving samples to update the output weight matrix, the proposed algorithm employs different strategies namely sample deletion, sample reserve and sample learning strategy to decide whether the data will be deleted directly, reserved for later use or used immediately. Instantaneous error is used to select the best learning strategy. The evaluation of McFELM is presented doing simulations on a nonlinear system identification problem and a set of benchmark regression problems from UCI machine leaning repository. The results show that the proposed McFELM produces better performance compared with existing algorithms.