Abstract
A performance analysis is presented of the minimal resource allocating network (MRAN) algorithm for online identification of nonlinear dynamic systems. Using nonlinear time-invariant and time-varying identification benchmark problems, MRAN's performance is compared with the online structural adaptive hybrid learning (ONSAHL) algorithm. Results indicate that the MRAN algorithm realises networks using fewer hidden neurons than the ONSAHL algorithm, with better approximation accuracy. Methods for improving the run-time performance of MRAN for real-time identification of nonlinear systems are developed. An extension to MRAN is presented, which utilises a winner neuron strategy and is referred to as the extended minimum resource allocating network (EMRAN). This modification reduces the computation load for MRAN and leads to considerable reduction in the identification time, with only a small increase in the approximation error. Using the same benchmark problems, results show that EMRAN is well suited for fast online identification of nonlinear plants.
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