Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Evolving neurocomputing systems for horticulture applications
Applied Soft Computing
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Evolving Connectionist Systems for Adaptive Sport Coaching
Neural Information Processing
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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The paper presents a methodology for adaptive modelling and discovery of dynamic relationship rules from continuous data streams. In dynamic processes, underlying rules may change over time and tracing these changes is a difficult task for computer modelling. Evolving fuzzy neural networks (EFuNN) are used for this purpose here. EFuNNs belong to the group of evolving connectionist systems (ECOS). These are information systems that learn from data in a supervised mode through on-line adaptive clustering and allow for rule extraction, each rule representing input-output relationship within a cluster of data. Extracted rules, after each consecutive chunk of data is entered into the system, are compared in order to discover new patterns of interaction between input and output variables. Thus the stability and plasticity of the investigated process are evaluated. The rules are also used for the prediction of future events. To illustrate the methodology, a mathematical example is used, along with two real case studies. The first case study is from Macroeconomics and the second one is from Bioinformatics.