The Experts below are selected from a list of 26322 Experts worldwide ranked by ideXlab platform
Giovanni L Sicuranza - One of the best experts on this subject based on the ideXlab platform.
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nonlinear system identification using quasi perfect Periodic Sequences
2016Co-Authors: Giovanni L Sicuranza, Alberto CariniAbstract:Perfect Periodic Sequences are currently used for modeling linear and nonlinear systems. A Periodic Sequence, applied as input to a linear or nonlinear system, is called perfect if the basis functions of the modeling filter are orthogonal to each other, and thus the auto-correlation matrix is diagonal. In this paper, we introduce quasi-perfect Periodic Sequences for a sub-class of linear-in-the-parameters nonlinear filters, called functional link polynomial filters, which is derived by using the constructive rule of Volterra filters. A Periodic Sequence is defined as quasi-perfect for a nonlinear filter if the resulting auto-correlation matrix is block-diagonal and highly sparse. Moreover, the samples of the Sequence are represented by only a few discrete levels. It is shown in the paper that quasi-perfect Periodic Sequences for third-order systems can be obtained by means of a simple combinatorial rule. The derived Sequences, which are the same for all functional link polynomial filters, allow an efficient implementation of the least-squares approximation method. Simulation results and a real-world experiment show good performance of the proposed identification method. HighlightsThe paper discusses functional link polynomial (FLiP) nonlinear filters.Quasi-perfect Periodic Sequences (QPPSs) are introduced for their identification.QPPSs have discrete level samples and block-diagonal sparse autocorrelation matrix.A simple and fast combinatorial rule is provided for generating the QPPSs.
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perfect Periodic Sequences for legendre nonlinear filters
2014Co-Authors: Alberto Carini, Stefania Cecchi, Laura Romoli, Giovanni L SicuranzaAbstract:The paper shows that perfect Periodic Sequences can be developed and used for the identification of Legendre nonlinear filters, a sub-class of linear-in-the-parameters nonlinear filters recently introduced in the literature. A Periodic Sequence is perfect for the identification of a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. Using perfect Periodic Sequences as input signals, the unknown nonlinear system and its most relevant basis functions can be identified with the cross-correlation method. The effectiveness and efficiency of this approach is illustrated with experimental results involving a real nonlinear system.
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perfect Periodic Sequences for even mirror fourier nonlinear filters
2014Co-Authors: Alberto Carini, Giovanni L SicuranzaAbstract:The paper deals with the identification of a class of nonlinear filters recently introduced in the literature, the even mirror Fourier nonlinear filters, and shows that perfect Periodic Sequences can be developed for these filters. A Periodic Sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. By applying perfect Periodic Sequences as input signals to even mirror Fourier nonlinear filters, it is possible to model unknown nonlinear systems using the cross-correlation method. Moreover, the most relevant basis functions, i.e., those that guarantee the most compact representation of the nonlinear system according to some information criterion, can be easily estimated. Experimental results on the identification of real nonlinear systems illustrate the main advantage of the proposed method which is the remarkable reduction in the computational complexity.
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perfect Periodic Sequences for identification of even mirror fourier nonlinear filters
2014Co-Authors: Alberto Carini, Giovanni L SicuranzaAbstract:In this paper we consider the identification of a class of linear-in-the parameters nonlinear filters that has been recently introduced, the so-called even mirror Fourier nonlinear filters. We show that perfect Periodic Sequences can be derived for these filters. A Periodic Sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. By applying perfect Periodic Sequences as input signals to even mirror Fourier nonlinear filters, it is possible to model unknown nonlinear systems exploiting the cross-correlation method. Then, the most relevant basis functions, i.e., those that guarantee the most compact representation of the nonlinear system according to some information criterion, can be easily estimated. Experimental results on the identification of a real nonlinear system illustrate the effectiveness of the proposed approach.
Alberto Carini - One of the best experts on this subject based on the ideXlab platform.
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nonlinear system identification using quasi perfect Periodic Sequences
2016Co-Authors: Giovanni L Sicuranza, Alberto CariniAbstract:Perfect Periodic Sequences are currently used for modeling linear and nonlinear systems. A Periodic Sequence, applied as input to a linear or nonlinear system, is called perfect if the basis functions of the modeling filter are orthogonal to each other, and thus the auto-correlation matrix is diagonal. In this paper, we introduce quasi-perfect Periodic Sequences for a sub-class of linear-in-the-parameters nonlinear filters, called functional link polynomial filters, which is derived by using the constructive rule of Volterra filters. A Periodic Sequence is defined as quasi-perfect for a nonlinear filter if the resulting auto-correlation matrix is block-diagonal and highly sparse. Moreover, the samples of the Sequence are represented by only a few discrete levels. It is shown in the paper that quasi-perfect Periodic Sequences for third-order systems can be obtained by means of a simple combinatorial rule. The derived Sequences, which are the same for all functional link polynomial filters, allow an efficient implementation of the least-squares approximation method. Simulation results and a real-world experiment show good performance of the proposed identification method. HighlightsThe paper discusses functional link polynomial (FLiP) nonlinear filters.Quasi-perfect Periodic Sequences (QPPSs) are introduced for their identification.QPPSs have discrete level samples and block-diagonal sparse autocorrelation matrix.A simple and fast combinatorial rule is provided for generating the QPPSs.
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perfect Periodic Sequences for legendre nonlinear filters
2014Co-Authors: Alberto Carini, Stefania Cecchi, Laura Romoli, Giovanni L SicuranzaAbstract:The paper shows that perfect Periodic Sequences can be developed and used for the identification of Legendre nonlinear filters, a sub-class of linear-in-the-parameters nonlinear filters recently introduced in the literature. A Periodic Sequence is perfect for the identification of a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. Using perfect Periodic Sequences as input signals, the unknown nonlinear system and its most relevant basis functions can be identified with the cross-correlation method. The effectiveness and efficiency of this approach is illustrated with experimental results involving a real nonlinear system.
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perfect Periodic Sequences for even mirror fourier nonlinear filters
2014Co-Authors: Alberto Carini, Giovanni L SicuranzaAbstract:The paper deals with the identification of a class of nonlinear filters recently introduced in the literature, the even mirror Fourier nonlinear filters, and shows that perfect Periodic Sequences can be developed for these filters. A Periodic Sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. By applying perfect Periodic Sequences as input signals to even mirror Fourier nonlinear filters, it is possible to model unknown nonlinear systems using the cross-correlation method. Moreover, the most relevant basis functions, i.e., those that guarantee the most compact representation of the nonlinear system according to some information criterion, can be easily estimated. Experimental results on the identification of real nonlinear systems illustrate the main advantage of the proposed method which is the remarkable reduction in the computational complexity.
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perfect Periodic Sequences for identification of even mirror fourier nonlinear filters
2014Co-Authors: Alberto Carini, Giovanni L SicuranzaAbstract:In this paper we consider the identification of a class of linear-in-the parameters nonlinear filters that has been recently introduced, the so-called even mirror Fourier nonlinear filters. We show that perfect Periodic Sequences can be derived for these filters. A Periodic Sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. By applying perfect Periodic Sequences as input signals to even mirror Fourier nonlinear filters, it is possible to model unknown nonlinear systems exploiting the cross-correlation method. Then, the most relevant basis functions, i.e., those that guarantee the most compact representation of the nonlinear system according to some information criterion, can be easily estimated. Experimental results on the identification of a real nonlinear system illustrate the effectiveness of the proposed approach.
Zhenkun Huang - One of the best experts on this subject based on the ideXlab platform.
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exponential attractor of κ almost Periodic Sequence solution of discrete time bidirectional neural networks
2010Co-Authors: Zhenkun Huang, Xinghua Wang, Yonghui XiaAbstract:Abstract In this paper, several criteria are obtained for the existence and exponential attractivity of a unique κ -almost Periodic Sequence solution of discrete-time bidirectional neural networks (BNNs). Our results generalize the corresponding results about almost Periodic Sequence solution in common sense. Finally, computer simulations illustrate the dynamic behavior of discrete-time bidirectional neural networks. It is shown that discretization step κ affects dynamical characteristics of bidirectional neural networks and our results on κ -almost Periodic Sequence solution lead to effective estimates of continuous-time neural networks during real-time computations.
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the existence and exponential attractivity of κ almost Periodic Sequence solution of discrete time neural networks
2007Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In the present paper, several sufficient conditions are obtained for the existence and exponential attractivity of a unique κ-almost Periodic Sequence solution of discrete time neural network. Our results generalize the corresponding results about almost Periodic Sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential attractivity of κ-almost Periodic Sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.
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the existence and global attractivity of almost Periodic Sequence solution of discrete time neural networks
2006Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In this Letter, we discuss discrete-time analogue of a continuous-time cellular neural network. Sufficient conditions are obtained for the existence of a unique almost Periodic Sequence solution which is globally attractive. Our results demonstrate dynamics of the formulated discrete-time analogue as mathematical models for the continuous-time cellular neural network in almost Periodic case. Finally, a computer simulation illustrates the suitability of our discrete-time analogue as numerical algorithms in simulating the continuous-time cellular neural network conveniently.
M A Ushakov - One of the best experts on this subject based on the ideXlab platform.
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forbidden substrings kolmogorov complexity and almost Periodic Sequences
2006Co-Authors: Yu A Rumyantsev, M A UshakovAbstract:Assume that for some a < 1 and for all nutural n a set F n of at most 2 αn forbidden binary strings of length n is fixed. Then there exists an infinite binary Sequence ω that does not have (long) forbidden substrings. We prove this combinatorial statement by translating it into a statement about Kolmogorov complexity and compare this proofl with a combinatorial one based on Laslo Lovasz local lemma. Then we construct an almost Periodic Sequence with the same property (thus combines the results from [1] and [2]). Both the combinatorial proof and Kolmogorov complexity argument can be generalized to the multidimensional case.
Xinghua Wang - One of the best experts on this subject based on the ideXlab platform.
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exponential attractor of κ almost Periodic Sequence solution of discrete time bidirectional neural networks
2010Co-Authors: Zhenkun Huang, Xinghua Wang, Yonghui XiaAbstract:Abstract In this paper, several criteria are obtained for the existence and exponential attractivity of a unique κ -almost Periodic Sequence solution of discrete-time bidirectional neural networks (BNNs). Our results generalize the corresponding results about almost Periodic Sequence solution in common sense. Finally, computer simulations illustrate the dynamic behavior of discrete-time bidirectional neural networks. It is shown that discretization step κ affects dynamical characteristics of bidirectional neural networks and our results on κ -almost Periodic Sequence solution lead to effective estimates of continuous-time neural networks during real-time computations.
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the existence and exponential attractivity of κ almost Periodic Sequence solution of discrete time neural networks
2007Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In the present paper, several sufficient conditions are obtained for the existence and exponential attractivity of a unique κ-almost Periodic Sequence solution of discrete time neural network. Our results generalize the corresponding results about almost Periodic Sequence solution in common sense. It is shown that discretization step κ affects the dynamical characteristics of discrete-time analogues of continuous time neural networks and exponential convergence is dependent on small discretization step size. Our results on exponential attractivity of κ-almost Periodic Sequence solution can provide us with relevant estimates on how precise such networks can perform during real-time computations. Finally, computer simulations are performed in the end to show the feasibility of our results.
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the existence and global attractivity of almost Periodic Sequence solution of discrete time neural networks
2006Co-Authors: Zhenkun Huang, Xinghua WangAbstract:In this Letter, we discuss discrete-time analogue of a continuous-time cellular neural network. Sufficient conditions are obtained for the existence of a unique almost Periodic Sequence solution which is globally attractive. Our results demonstrate dynamics of the formulated discrete-time analogue as mathematical models for the continuous-time cellular neural network in almost Periodic case. Finally, a computer simulation illustrates the suitability of our discrete-time analogue as numerical algorithms in simulating the continuous-time cellular neural network conveniently.