Abstract-This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on -most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: 1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and 2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.Index Terms-Dynamic evolving neural-fuzzy inference system (DENFIS), hybrid systems, online adaptive learning, online clustering, time series prediction.
Contemporary models of intrafibrillar mineralization mechanisms are established using collagen fibrils as templates without considering the contribution from collagen-bound apatite nucleation inhibitors. However, collagen matrices destined for mineralization in vertebrates contain bound matrix proteins for intrafibrillar mineralization. Negatively charged, high–molecular weight polycarboxylic acid is cross-linked to reconstituted collagen to create a model for examining the contribution of collagen-ligand interaction to intrafibrillar mineralization. Cryogenic electron microscopy and molecular dynamics simulation show that, after cross-linking to collagen, the bound polyelectrolyte caches prenucleation cluster singlets into chain-like aggregates along the fibrillar surface to increase the pool of mineralization precursors available for intrafibrillar mineralization. Higher-quality mineralized scaffolds with better biomechanical properties are achieved compared with mineralization of unmodified scaffolds in polyelectrolyte-stabilized mineralization solution. Collagen-ligand interaction provides insights on the genesis of heterogeneously mineralized tissues and the potential causes of ectopic calcification in nonmineralized body tissues.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.