English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choicesamong biocompatible materials for industrial and biomedical applications.Despite their advantages, some of their drawbacks include non-linear andhysteretic behavior, which complicates the modeling process. In previous works,usually autoregressive models were used to predict the behavior of an IPMCactuator. The main drawback of using an autoregressive model is that it cannotbe used in mobile and real-time applications. In this study, we proposed ahybrid analytical intelligent model for an IPMC actuator. The most outstandingfeature of this model is its non-autoregressive structure. The hybrid conceptproposed in this study can be generalized to various problems other than IPMCs.The structure used in this work comprises an analytical model and a deep neuralnetwork, providing a non-linear, dynamic, and non-autoregressive model for theIPMC actuator. Lastly, the average NMSE achieved using the proposed hybridmodel is 9.5781e-04 showing a significant drop in the error rate compared toother non-autoregressive structures.

Physics-Data Driven Machine Learning Based Model: A Hybrid Way for Nonlinear, Dynamic, and Open-loop Identification of IPMC Soft Artificial Muscles