Abstract
A neural-network-based fuzzy system (NNFS) is proposed in this paper. It is a self-organizing neural-network which can partition the input spaces in a flexible way, based on the distribution of the training data in order to reduce the number of rules without any loss of modeling accuracy. Associated with the NNFS is a two-phase hybrid learning algorithm which utilizes a nearest-neighborhood clustering scheme for both structure learning and initial parameters setting and a gradient descent method for fine tuning the parameters of the NNFS. By combining the above two methods, the learning speed converges much faster than the original back-propagation algorithm. Simulation results suggest that the NNFS has merots of simple structure, fast learning speed, few fuzzy logic rules and relatively high modeling accuracy. Finally, the NNFS is applied to the construction of the soft sensor for a distillation column.
Published Version
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