3-dimensional object recognition by evolutional RBF network

This paper tries to recognize 3-dimensional objects by using an evolutional RBF network. Our proposed RBF network has the structure of preparing four RBFs for each hidden layer unit, selecting based on the Euclid distance between an input image and RBF. This structure can be invariant to 2- dimensional rotation by 90 degree. The other rotational invariance can be achieved by the RBF network. In hidden layer units, the number of RBFs, form, and arrangement are determined using real-coded GA. Computer simulations show object recognition can be done using such a method.