Data-Driven Fuzzy Modeling Using Deep Learning
For researchers in fuzzy modeling and nonlinear system identification, this work offers a hybrid approach that addresses rule extraction and parameter training, but the results are incremental.
The paper proposes a data-driven fuzzy modeling method that combines deep learning (RBM), probability-based clustering, and extreme learning machines to extract and train fuzzy rules, validated on two benchmark problems.
Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machines. We use the restricted Boltzmann machine (RBM) and probability theory to overcome some common problems in data based modeling methods. The RBM is modified such that it can be trained with continuous values. A probability based clustering method is proposed to partition the hidden features from the RBM, and extract fuzzy rules with probability measurement. An extreme learning machine and an optimization method are applied to train the consequent part of the fuzzy rules and the probability parameters. The proposed method is validated with two benchmark problems.