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This article discusses the practical aspects of using neural networks in the identification of nonlinear industrial plants through the use of closed-loop operation data, with the purpose of creating a computational model that facilitates the development and tuning of intelligent control algorithms. In this paper, specifically, an oil separation plant is identified and the resulting model is employed...
Reference evapotranspiration (ETo) is an important factor in the water-saving agriculture and in the soil-plant-atmosphere continuum. Many machine learning methods have been introduced into predicting ETo. In order to improve the accuracy of radiation-based ETo models, this paper presents an ETo model called RFR based on random forest (RF). By taking the results of FAO Penman-Monteith (FAOPM) model...
This paper reports on the development of a novel neural network (NN) based airspeed estimator, and focuses on system design. In particular, a novelty detector, capable of preventing the system from producing an erroneous output during conditions in which it is known that a NN model will be extrapolating, is included within the design. A simulation model of a single main rotor helicopter was used to...
Artificial neural networks have been getting popularity for predicting various performance parameters of microstrip antennas due to their learning and generalization features. In this letter, a neural-networks-based synthesis model is presented for predicting the “slot-size” on the radiating patch and inserted “air-gap” between the ground plane and the substrate sheet, simultaneously. Different performance...
Fault detection and isolation are one of the most important steps in automotive diagnosis. In this work, a new OBD scheme is proposed dealing with fault detection and localization problem in diesel engine. Especially, the leak detection and characterization problem in diesel air path is studied. The proposed solution is based on the neural network trained using Levenberg-Marquardt algorithm in order...
This research was developed in a greenhouse located in Mexico, in which there are big variations in temperature and relative humidity, generating production losses. Consequently a good greenhouse control tool was necessary to keep these variables inside of the optimal levels. Black box models have been applied in this greenhouse to predict temperature and relative humidity, however they fail in relative...
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