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Assisted Research of the Dynamic Neural Networks with Time-Delays and Recurrent Links
Abstract:
The paper showed the assisted research of one new model of digital dynamic neural network by using the LabVIEW proper virtual instrumentation and proper mathematical model. In the research were used some different way to optimize the convergence process, for example: using one time- delay of the first and second output from the neural layers; using the recursive link and time- delay; using the bipolar sigmoid hyperbolic tangent sensitive function replacing the sigmoid simple sensitive function. By on-line simulation of the neural network it is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and time- delay, to the gradient errors, in a convergence process. By on-line using the proper virtual LabVIEW instrumentation, were established some influences of the network parameters: number of input vector data, number of neurons in each layers, to the number of iterations before canceled the mean square error to the target. In the optimization research we used the minimization of the gradient error function between the output and the target.
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Periodical:
Pages:
1094-1097
Citation:
Online since:
February 2012
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