On line learning fuzzy rule-based system structure from data streams

A new approach to fuzzy rule-based systems structure identification in on-line (possibly real-time) mode is described in this paper. It expands the so called evolving Takagi-Sugeno (eTS) approach by introducing self-learning aspects not only to the number of fuzzy rules and system parameters but also to the number of antecedent part variables (inputs). The approach can be seen as on-line sensitivity analysis or on-line feature extraction (if in a classification application, e.g. in eClass which is the classification version of eTS). This adds to the flexibility and self-learning capabilities of the proposed system. In this paper the mechanism of formation of new fuzzy sets as well as of new fuzzy rules is analyzed from the point of view of on-line (recursive) data density estimation. Fuzzy system structure simplification is also analyzed in on-line context. Utility- and age-based mechanisms to address this problem are proposed. The rule-base structure evolves based on a gradual update driven by; i) information coming from the new data samples; ii) on-line monitoring and analysis of the existing rules in terms of their utility, age, and variables that form them. The theoretical theses are supported by experimental results from a range of real industrial data from chemical, petro-chemical and car industries. The proposed methodology is applicable to a wide range of fault detection, prediction, and control problems when the input or feature channels are too many.