Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments


Ionic polymer-metal composites (IPMCs) are especially interesting electroactive polymers because they show large a deformation in the presence of a very low driving voltage (around 1 - 2 V) and several applications have recently been proposed. Normally a humid environment is required for the best operation, although some IPMCs can operate in a dry environment, after proper encapsulation or if a solid electrolyte is used in the manufacturing process. However, such solutions usually lead to increasing mechanical stiffness and to a reduction of actuation capabilities. In this study we focus on the behaviour of non-encapsulated IPMCs as actuators in dry environments, in order to obtain relevant information for design tasks linked to the development of active devices based on this kind of smart material. The non-linear response obtained in the characterisation tests is especially well-suited to modelling these actuators with the help of artificial neural networks (ANNs). Once trained with the help of characterisation data, such neural networks prove to be a precise simulation tool for describing IPMC response in dry environments.

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A. Díaz Lantada, P. Lafont Morgado, J. Luis Muñoz Sanz, J. Manuel Muñoz Guijosa and J. Echávarri Otero, "Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments," Journal of Signal and Information Processing, Vol. 3 No. 2, 2012, pp. 137-145. doi: 10.4236/jsip.2012.32018.

Conflicts of Interest

The authors declare no conflicts of interest.


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