Knowledge-based neurocomputing
Knowledge-based neurocomputing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Neural networks-based scheme for system failure detection and diagnosis
Mathematics and Computers in Simulation
A New Data Clustering Approach for Data Mining in Large Databases
ISPAN '02 Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks
A hybrid clustering and gradient descent approach for fuzzymodeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Using a non-uniform self-selective coder for option pricing
Applied Soft Computing
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
A vector forecasting model for fuzzy time series
Applied Soft Computing
A GMDH-based fuzzy modeling approach for constructing TS model
Fuzzy Sets and Systems
Engineering Applications of Artificial Intelligence
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
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This paper introduces a dynamic evolving computation system (DECS) model, for adaptive on-line learning, and its application for dynamic time series prediction. DECS evolve through evolving clustering method and evolutionary computation for structure learning, Levenberg-Marquardt method for parameter learning, learning and accommodate new input data. DECS is created and updated during the operation of the system. At each time moment the output of DECS is calculated through a knowledge rule inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. An approach is proposed for a dynamic creation of a first order Takagi-Sugeno type fuzzy rule set for the DECS model. The fuzzy rules can be inserted into DECS before, or during its learning process, and the rules can also be extracted from DECS during, or after its learning process. It is demonstrated that DECS can effectively learn complex temporal sequences in an adaptive way and outperform some existing models.