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In this paper, training the derivative of a feedforward neural network with the extended backpropagation algorithm is presented. The method is used to solve a class of first-order partial differential equations for input-to-state linearizable or approximate linearizable systems. The solution of the differential equation, together with the Lie derivatives,(More)
In this paper several schemes for feedback linearization using neural networks have been investigated and compared. Then an approach to design a neurocontroller in the sense of feedback linearization is introduced. The contents include: 1) full input-output linearization when a system has relative degree n; 2) partial input-output linearization when a(More)
The essay outlines one particular possibility of efficient evaluating the Performance of edge detector algorithms. Three generally known and published algorithms (Canny, Marr, Shen) were analysed by way of example. The analysis is based on two-dimensional signals created by means of two-dimensional Semi-Markov Model and subsequently provided with an(More)