Incremental modeling with rough and fine tuning method

In this paper, we propose a new learning approach for designing an incremental model that has a cascade learning structure combined with a rough and fine tuning method for the learning scheme. Recently, various fuzzy logic-based modeling methods, with fuzzy if-then type rules, have been proposed in an attempt to obtain good approximations and generalization performances. In contrast to these various modeling methods, the new proposed incremental modeling scheme presented here is combined with a rough and fine tuning scheme, to learn and construct the best architecture for the model. A compensation idea is introduced in the fine tuning stage to solve the over-fitting problem caused from testing data. For this purpose, a construct of an extreme learning machine (ELM) is used as a global model, and this is compensated through a conditional fuzzy C-means (CFCM)-based fuzzy inference system (FIS) with a Takagi–Sugeno–Kang (TSK)-type method, which captures the remaining localized nonlinearities of the model. The experimental results, obtained by the proposed model have proved to show better performances in comparison with previous works.