A stochastic reverse interpolation algorithm for real-valued function learning

A learning algorithm for a pRAM-based network is presented in which the neural network learns real-valued functions by a stochastic reinforcement rule with linear reverse interpolation. The algorithm uses the output spike trains to approximate function values and to update memory contents. The algorithm is hardware realisable. It finds applications in areas which involve real-time learning, such as robotics and control.<
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