Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Soft Computing and Fuzzy Logic
IEEE Software
Vagueness evaluation of the crisp output in a fuzzy inference system
Fuzzy Sets and Systems
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
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"The only thing constant is change."--Ray Kroc (Founder of McDonald's). Self-organizing neuro-fuzzy machines are maturing in their online learning process for time-invariant conditions. To, however, maximize the operative value of these self-organizing approaches for onlinereasoning, such self-sustaining mechanisms must embed capabilities that aid the reorganizing of knowledge structures in real-time dynamic environments. Also, neuro-fuzzy machines are well-regarded as approximate reasoning tools because of their strong tolerance to imprecision and handling of uncertainty. Recently, Tan and Quek (2010) discussed an online self-reorganizing neuro-fuzzy approach called SeroFAM for financial time-series forecasting. The approach is based on the BCM theory of neurological learning via metaplasticity principles (Bienenstock et al., 1982), which addresses the stability limitations imposed by the monotonic behavior in Hebbian theory for online learning (Rochester et al., 1956). In this paper, we examine an adapted version called iSeroFAM for interval-forecasting of financial time-series that follows a computational efficient approach adapted from Lalla et al. (2008) and Carlsson and Fuller (2001). An experimental proof-of-concept is presented for interval-forecasting of 80 years of Dow Jones Industrial Average Index, and the preliminary findings are encouraging.