eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
Fuzzy multivariable Gaussian evolving approach for fault detection and diagnosis
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
A new systematic design for Habitually Linear Evolving TS Fuzzy Model
Expert Systems with Applications: An International Journal
A data-model-fusion prognostic framework for dynamic system state forecasting
Engineering Applications of Artificial Intelligence
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Evolving Takagi-Sugeno fuzzy model based on switching to neighboring models
Applied Soft Computing
A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
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A reliable and online predictor is very useful to a wide array of industries to forecast the behavior of time-varying dynamic systems. In this paper, an evolving fuzzy system (EFS) is developed for system state forecasting. An evolving clustering algorithm is proposed for cluster generation. Clusters are established and modified based on constraint criteria of mapping consistence and compatible measurement. A novel recursive Levenberg-Marquardt (R-LM) method is proposed for online training of nonlinear EFS parameters. The viability of the developed EFS predictor is evaluated based on both simulation from benchmark data and real-time tests corresponding to machinery condition monitoring and material property testing. Test results show that the developed EFS predictor is an effective and accurate forecasting tool. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. The proposed clustering algorithm is an effective structure identification method. The recursive training technique is computationally efficient, and can effectively improve reasoning convergence.