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The paper proposes a methodology to self-evolve an online fuzzy logic controller (FLC). The proposed methodology does not require any initialization at all, it can start with an empty set of fuzzy control rules or with a simple collection of fuzzy control rules obtained from an expert operator. The FLC design is online, using only the input/output data obtained during the normal operation of the system...
This paper proposes a method for adaptive identification and predictive control using an online sequential extreme learning machine based on the recursive partial least-squares method (OS-ELM-RPLS). OL-ELM-RPLS is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OS-ELM-RLS...
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 mixture of univariate linear regression models (MULRM) to be applied in time-varying scenarios, and its application to soft sensor problems. Offline and online solutions of MULRM will be obtained using the Expectation-Maximization Algorithm. A forgetting factor will be introduced in the online solution to discount the information of already learned data, so that it can be applied...
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...
In this work, two alternative methodologies for modeling and predicting gas emissions of NO, NO2 and SO2 are presented. The first method involves hard modeling strategies with Parsimonious Multivariate Least Squares (PMLS) assuming simple polynomial functions as base model. The second is a soft modeling approach using Extreme Learning Machine (ELM). In this work we found that both methods have similar...
This paper proposes EventCluster, a novel approach in real-time data modeling. It deploys the Rank Order Clustering (ROC) method to automatically group all existing data sensors and actuators of the system to the Key Performance Indicators of the system. EventCluster (EC) is a cause-effect relationship data clustering tool that detects the interrelationship between field data and system performance...
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