Proposes a path-based neural network algorithm called recurrent continuous backpropagation two (RCBP-2) for complex sequential processing using a gradient descent method. Under the path-based approach, the goal weights are a collection of weight states. Coupled with the underlying continuity of training exemplars and sequential nature of the system attributes, RCBP-2 can achieve arbitrarily close approximations of complex trajectories within a fixed and relatively small network topology. The performance of RCBP-2 is also monitored by training and subsequently testing on a 4-orbits problem. The results show that RCBP-2 results in a fast and efficient algorithm for complex sequential processing.
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