Titlebook: Identification of Nonlinear Systems Using Neural Networks and Polynomial Models; A Block-Oriented App Andrzej Janczak Book 2005 Springer-Ve

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书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models影响因子(影响力)




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models影响因子(影响力)学科排名




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models网络公开度




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models网络公开度学科排名




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models被引频次




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models被引频次学科排名




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models年度引用




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models年度引用学科排名




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models读者反馈




书目名称Identification of Nonlinear Systems Using Neural Networks and Polynomial Models读者反馈学科排名

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6 Applications,ve stage sugar evaporation station is studied in Section 6.3. Two nominal models of the process, i.e., a linear model and a neural network Wiener model are developed based on the real process data recorded at the Lublin Sugar Factory in Poland. Finally, Section 6.4 summarizes the results.

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Book 2005known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new a

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0170-8643 This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gi

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4 Polynomial Wiener models,an approach, however, results in inconsistent parameter estimates. As a remedy against this problem, an instrumental variables method is proposed with instrumental variables chosen as delayed system inputs and delayed and powered delayed outputs of the model obtained using the least squares method.

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hardness studies the vastly dominant technique is that of static indentation. Now some practical aspects of indentation hardness measurements will be considered. Figure 1.3 shows that in a seemingly arbitrary way indentation hardness values and measurements have been determined by the range of the a

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Andrzej Janczakhardness studies the vastly dominant technique is that of static indentation. Now some practical aspects of indentation hardness measurements will be considered. Figure 1.3 shows that in a seemingly arbitrary way indentation hardness values and measurements have been determined by the range of the a

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