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The paper proposes a new framework to learn a Fuzzy Logic Controller (FLC), from data extracted from a process while it is being manually controlled, in order to control nonlinear industrial processes. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA). First, the fuzzy c-means (FCM) clustering algorithm is applied to initialize the HGA population, in order to reduce the...
This paper proposes a learning algorithm for single-hidden layer feedforward neural networks (SLFN) called genetically optimized extreme learning machine (GO-ELM). In the GO-ELM, the structure and the parameters of the SLFN are optimized by a genetic algorithm (GA). The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov's regularization in order to...
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