By Topic

Nonlinear time series model for shape classification using neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Shenshu Xiong ; Department of Precision Instruments and Mechanology Tsinghua University Beijing 100084 China ; Zhaoying Zhou

A complex nonlinear exponential autoregressive CNEAR model for invariant feature extraction is developed for recognizing arbitrary shapes on a plane. A neural network is used to calculate the CNEAR coefficients. The coefficients which constitute the feature set are proven to be invariant to boundary transformations such as translation rotation scale and choice of starting point in tracing the boundary. The feature set is then used as the input to a complex multilayer perceptron C-MLP network for learning and classification. Experimental results show that complicated shapes can be accurately recognized even with the low-order model and that the classification method has good fault tolerance when noise is present.

Published in:

Tsinghua Science and Technology  (Volume:5 ,  Issue: 4 )