A resource-allocating network for function interpolation
Neural Computation
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Product Demand Forecasting with a Novel Fuzzy CMAC
Neural Processing Letters
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel CMAC With Improved Capability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Neural Networks for Continuous Online Learning and Control
IEEE Transactions on Neural Networks
Efficient advertisement discovery for audio podcast content using candidate segmentation
EURASIP Journal on Audio, Speech, and Music Processing
Evolutionary FCMAC-BYY applied to stream data analysis
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Neurocomputing
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.