New skill learning paradigm using various kinds of neurons

Modeled from human neurons, various types of artificial neurons are developed and applied to control algorithm. In this paper, the weights and structure of feedforward neural network controller are updated using new skill learning paradigm which consists of supervisory controller, chaotic neuron filter and associative memory. The pattern of system nonlinearity along the desired path is extracted while supervisory controller guarantees stability in the sense of the boundedness of tracking error. Next the pattern is divided into small segments and encoded to bipolar codes depending on the existence of critical points. Comparing the encoded pattern with pre-stored neural parameters and pattern pairs through associative memory, the most similar one is obtained. Also, chaotic neuron filter is used to add perturbation to neural parameters when the training of feedforward neural network is not successful with the pre-stored parameters. Finally the memory is updated with new successful parameters and pattern pairs. Simulation is performed for simple two-link robot in case of the slight modification of desired trajectory.