Improvements in the accuracy of an Inverse Problem Engine's output for the prediction of below-knee prosthetic socket interfacial loads

This source preferred by John Vinney, Siamak Noroozi and Philip Sewell

Authors: Sewell, P., Noroozi, S., Vinney, J., Amali, R. and Andrews, S.

Journal: Engineering Applications of Artificial Intelligence

Volume: 23

Issue: 6

Pages: 1000-1011

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2010.02.011

The monitoring of in-service loads on many components has become a routine operation for simple and well-understood cases in engineering. However, as the complexity of the structure increases so does the difficulty in obtaining an acceptable understanding of the real loading. It has been shown that it is possible to solve these problems by interfacing traditional analysis methodologies with more modern mathematical methods (i.e. artificial intelligence) in order to create a hybrid analysis tool. It has, however, been recognised that an Artificial Neural Network (ANN) predicts poorly in the high and low ranges of the envelope of which it is trying to predict. This paper presents results of research to develop the ANN Difference Method to improve the accuracy of the Inverse Problem Engine's output. This method has been applied to accurately predict the complex pressure distribution at the residual limb/socket interface of a lower-limb prosthesis. It has been shown that application of the ANN Difference Method to the output of a backpropagation neural network can reduce inherent errors that exist at the low and high ends of the ANN solution envelope. This powerful approach can offer load information at high speed once the relationship between the loading and response of the component has been established through training the ANN. Utilising an experimental technique combined with an ANN can provide in-service loads on complex components in real time as part of a sophisticated embedded system.

This data was imported from DBLP:

Authors: Sewell, P., Noroozi, S., Vinney, J., Amali, R. and Andrews, S.

Journal: Eng. Appl. of AI

Volume: 23

Pages: 1000-1011

This data was imported from Scopus:

Authors: Sewell, P., Noroozi, S., Vinney, J., Amali, R. and Andrews, S.

Journal: Engineering Applications of Artificial Intelligence

Volume: 23

Issue: 6

Pages: 1000-1011

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2010.02.011

The monitoring of in-service loads on many components has become a routine operation for simple and well-understood cases in engineering. However, as the complexity of the structure increases so does the difficulty in obtaining an acceptable understanding of the real loading. It has been shown that it is possible to solve these problems by interfacing traditional analysis methodologies with more modern mathematical methods (i.e. artificial intelligence) in order to create a hybrid analysis tool. It has, however, been recognised that an Artificial Neural Network (ANN) predicts poorly in the high and low ranges of the envelope of which it is trying to predict. This paper presents results of research to develop the ANN Difference Method to improve the accuracy of the Inverse Problem Engine's output. This method has been applied to accurately predict the complex pressure distribution at the residual limb/socket interface of a lower-limb prosthesis. It has been shown that application of the ANN Difference Method to the output of a backpropagation neural network can reduce inherent errors that exist at the low and high ends of the ANN solution envelope. This powerful approach can offer load information at high speed once the relationship between the loading and response of the component has been established through training the ANN. Utilising an experimental technique combined with an ANN can provide in-service loads on complex components in real time as part of a sophisticated embedded system. © 2010 Elsevier Ltd. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Sewell, P., Noroozi, S., Vinney, J., Amali, R. and Andrews, S.

Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Volume: 23

Issue: 6

Pages: 1000-1011

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2010.02.011

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