  This paper describes a new approach for approximating the inverse kinematics
of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial
Basis Function Network). In this paper, a training solution using the ACO and
the LMS (Least Mean Square) algorithm is presented in a two-phase training
procedure. To settle the problem that the cluster results of k-mean clustering
Radial Basis Function (RBF) are easy to be influenced by the selection of
initial characters and converge to a local minimum, Ant Colony Optimization
(ACO) for the RBF neural networks which will optimize the center of RBF neural
networks and reduce the number of the hidden layer neurons nodes is presented.
The result demonstrates that the accuracy of Ant Colony Optimization for the
Radial Basis Function (RBF) neural networks is higher, and the extent of
fitting has been improved.
