Online learning in radial basis function networks
Neural Computation
Automatic generation of fuzzy rule-based models from data by genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Combination of Vector Quantization and Hidden Markov Models for Arabic Speech Recognition
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Sequential learning in neural networks: A review and a discussion of pseudorehearsal based methods
Intelligent Data Analysis
An architecture-adaptive neural network online control system
Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Robust adaptive neural networks with an online learning technique for robot control
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
An Evolutionary Approach Toward Dynamic Self-Generated Fuzzy Inference Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy Rules for Relaxed-Criteria Negotiation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Data-driven linguistic modeling using relational fuzzy rules
IEEE Transactions on Fuzzy Systems
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In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.