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A new algorithm used in large-scope solution is put forward to solve nonlinear equations which were not solved by some traditional methods. The initial value was arbitrarily chosen in very large scope. The convergence theorem of the algorithm was presented and proved. The computation is carried out by simple steepest descent rule without evaluation of the derivative evaluation of f. Thus, computation...
In this paper, we proposed a neural network algorithm for solving nonlinear equations. The convergence of algorithm proposed was researched. The convergence theorem provides the theory criterion selecting learning rate of neural network. The specific examples showed that the proposed method can solve nonlinear equations at a very rapid convergence and very high accuracy with less computation.
The problem of the offset thermal drift affects the measurement accuracy of pressure sensor in practical applications. To solve the problem, we proposed an approach of the non-linear compensation of pressure sensor using the neural network algorithm with Chebyshev basis functions. The convergence of the neural network algorithm is researched. To validate the validity of the algorithm, the example...
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