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When faced with the struggle to extract insights from complex and noisy data, often the end user may assume that there exist no significant relation between the features and target in the dataset and is forced to either quit the study or resort to alternate means. Artificial Neural Networks (ANNs) might be of help to predict some of the most complex data used in the industry. But it is neither easy...
A new strategy to realize an FPGA implementation of a soft sensor for an industrial process is proposed. In order to cope with the problem of small data sets in the identification of a non linear model the proposed approach is based on the integration of bootstrap re-sampling, noise injection and stacked neural networks (NNs), using the Principal Component Analysis (PCA). The aggregated final NN-PCA...
Aiming at some problems such as ldquoover-fitrdquo with ANN modeling, a new type of combined neural network and corresponding optimal modeling method are proposed in this paper. By this method, expectation error (namely, possible minimum error) is firstly estimated according to data quality; then, optimize network structure and choose optimal training result according to the difference between actual...
This paper proposed a new direct nonlinear controller design method based on virtual reference(VR) and support vector machine(SVM), which allows to directly design nonlinear controller on the base of input/output data with no need of a model of the plant. Firstly, the relation between virtual reference feedback tuning(VRFT) and internal model control(IMC) was analyzed. Then, the structure and design...
The back propagation training algorithm, used to train non-linear feed forward multi-layer artificial neural networks, is capable of estimating the error present in the data presented to a network. While of no use during the training of a network, such information can be useful after training to permit the input data to be itself adjusted to better fit the internal model of a trained neural network...
How to effectively monitor water quality of Coastal and inland waters by optical remote sensing has always been a difficulty. This study develops a neural network model to improve the accuracy in monitoring water quality of Lake Taihu, China, a large shallow subtropical lake. A three-layer back-propagation neural network is built up to estimate concentrations of chlorophyll-a, total suspended matter...
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