Principal Component Analysis and Automatic Relevance Determination in Damage Identification

Details Principal Component Analysis and Automatic Relevance Determination in Damage Identification Principal Component Analysis and Automatic Relevance Determination in Damage Identification

2007-05-11

arxiv.org/abs/0705.1672v1

Computer Science

Computational Engineering, Finance, and Science

6 pages

Scientific paper

This paper compares two neural network input selection schemes, the Principal Component Analysis (PCA) and the Automatic Relevance Determination (ARD) based on Mac-Kay's evidence framework. The PCA takes all the input data and projects it onto a lower dimension space, thereby reduc-ing the dimension of the input space. This input reduction method often results with parameters that have significant influence on the dynamics of the data being diluted by those that do not influence the dynamics of the data. The ARD selects the most relevant input parameters and discards those that do not contribute significantly to the dynamics of the data being modelled. The ARD sometimes results with important input parameters being discarded thereby compromising the dynamics of the data. The PCA and ARD methods are implemented together with a Multi-Layer-Perceptron (MLP) network for fault identification in structures and the performance of the two methods is as-sessed. It is observed that ARD and PCA give similar accu-racy levels when used as input-selection schemes. There-fore, the choice of input-selection scheme is dependent on the nature of the data being processed.

Affiliated with

Also associated with

No associations

Rating

Principal Component Analysis and Automatic Relevance Determination in Damage Identification does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Principal Component Analysis and Automatic Relevance Determination in Damage Identification, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Principal Component Analysis and Automatic Relevance Determination in Damage Identification will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-411708