Reference health

A recurrent neural network–based model for predicting bending behavior of ionic polymer–metal composite actuators

https://doi.org/10.1177/1045389x20942318
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39/39 checkable references clean · checked 2026-07-23

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

10 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 39 checked references that resolve
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Nonautoregressive Nonlinear Identification of IPMC in Large Deformation Situations Using Generalized Volterra-Based Approach
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From modeling to implementation of a method for restraining back relaxation in ionic polymer–metal composite soft actuators
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A new approach to develop ionic polymer–metal composites (IPMC) actuator: Fabrication and control for active catheter systems
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Universal Concept for Fabricating Arbitrary Shaped μIPMC Transducers and Its Application on Developing Accurately Controlled Surgical Devices
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Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter
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Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
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Identification of the Nonlinear Response of Ionic Polymer Actuators using the Volterra Series
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Water uptake and migration effects of electroactive ion-exchange polymer metal composite (IPMC) actuator
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Control of IPMC Actuators for Microfluidics With Adaptive “Online” Iterative Feedback Tuning
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An Algorithm for Least-Squares Estimation of Nonlinear Parameters
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Identification of an ionic polymer metal composite actuator employing Preisach type fuzzy NARX model and Particle Swarm Optimization
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ACTIVE POLYELECTROLYTE GELS AS ELECTRICALLY CONTROLLABLE ARTIFICIAL MUSCLES AND INTELLIGENT NETWORK STRUCTURES
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The 10 references without a DOI — listed, not checked
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no DOI — not checkedbibr9-1045389X20942318
no DOI — not checkedA guide to recurrent neural networks and backpropagation
no DOI — not checkedProceedings of IEEE International Conference in Control, Automation and Systems (ICCAS)
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no DOI — not checkedNeural Network Design
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no DOI — not checkedNonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
no DOI — not checkedPatent No5268082
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What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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