Introducing an Evolving Local Neuro-Fuzzy Model - Application to modeling of car-following behavior

•Proposing An Evolving Model for identification of nonlinear time-varying systems is proposed.•An EHBT-D learning algorithm for evolution or adaptation is developed.•The RWLS algorithm with adaptive forgetting factor is employed.•A dependable solution is proposed to model the car-following behavior.

This paper proposes an Evolving Local Linear Neuro-Fuzzy Model for modeling and identification of nonlinear time-variant systems which change their nature and character over time. The proposed approach evolves through time to follow the structural changes in the time-variant dynamic systems. The evolution process is managed by a distance-based extended hierarchical binary tree algorithm, which decides whether the proposed evolving model should be adapted to the system variations or evolution is necessary. To represent an interesting but challenging example of the systems with changing dynamics, the proposed evolving model is applied to model car-following process in a traffic flow, as an online identification problem. Results of simulations demonstrate effectiveness of the proposed approach in modeling of the time-variant systems.