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A prediction model of etch microtrenching was constructed by using a neural network.
This paper deals with the problem of response approximation of mechanical structures by using a neural network.
A prediction model of microtrenching depth (MD) was constructed by using a neural network and a genetic algorithm.
In this paper, a parameter estimation scheme for nonlinear systems is proposed by using a neural network.
By using a neural net to classify measurement or constraint residuals, gross errors in the data can be identified accurately and efficiently.
The description of the contacting load is then interpreted into meaningful descriptors typically by using a neural network or fuzzy rules.
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The stable and precise positioning control of the stage was achieved by using a neural-fuzzy controller.
In the PSO-NTVENN method, parameters are determined by using a sequential neural network approximation.
Excellent results were obtained in modelling the relation between froth characteristics or froth grade and recovery by using a backpropagation neural network.
This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network.
In this paper, we devised an analog circuit for the weighted least-squares (WLS) design of FIR Nyquist filters by using a Hopfield neural network (HNN).
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