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eFSM, SeroFAM, SaFIN - Coggle Diagram
eFSM
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Problem Statement
Fuzzy models face constraints in adapting to dynamically changing environments. These models are constructed and trained assuming that underlying data-generating processes remain static over time.
Limited ability to incorporate new information post-training leads to the necessity of retraining the model with updated data. The stability-plasticity dilemma describes the phenomenon where the system's fixed learning capacity results in degraded computing performance and the loss of previously learned knowledge when accommodating new information.
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SeroFAM
Problem Statement
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Absence of a comprehensive understanding of the BCM theory of meta-plasticity for associative learning in the context of self-organizing neurofuzzy systems.
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Conclusion
Tested on
time-variant S&P-500 market index over a period of about 60 years -> reorganize its fuzzy associations once in every 2–3 years from 1980 to 2009
under time-invariant conditions, produce a fuzzy approximation for a nonlinear plant system, achieve a reasonable representation of the plant using simple fuzzy approximates
SaFIN
Problem Statement
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Issues related to self-organization of numerical training data, including inconsistent rulebases, the need for prior knowledge like the number of clusters, heuristically designed knowledge acquisition methodologies, and the stability-plasticity tradeoff of the system.
The need for a more efficient, automated, and data-driven approach for the formulation of the fuzzy rulebase and clustering techniques in the design of neural fuzzy systems.
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