The Experts below are selected from a list of 91356 Experts worldwide ranked by ideXlab platform
G. Panda - One of the best experts on this subject based on the ideXlab platform.
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identification of nonlinear dynamic systems using functional link artificial neural networks
Systems Man and Cybernetics, 1999Co-Authors: Jagdish C Patra, Ranendra N Pal, B N Chatterji, G. PandaAbstract:We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the Input Pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.
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Nonlinear channel equalization for QAM signal constellation using artificial neural networks
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 1999Co-Authors: J.c. Patra, R. Baliarsingh, G. PandaAbstract:Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the Input Pattern by trigonometric polynomials. Because of Input Pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex Pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of Input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.
Jiashu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network
Neurocomputing, 2009Co-Authors: Haiquan Zhao, Jiashu ZhangAbstract:A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the Input Pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification. © 2009.
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functional link neural network cascaded with chebyshev orthogonal polynomial for nonlinear channel equalization
Signal Processing, 2008Co-Authors: Haiquan Zhao, Jiashu ZhangAbstract:Nonlinear intersymbol interference (ISI) leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel computationally efficient functional link neural network cascaded with Chebyshev orthogonal polynomial is proposed to combat nonlinear ISI. The equalizer has a simple structure in which the nonlinearity is introduced by functional expansion of the Input Pattern by trigonometric polynomial and Chebyshev orthogonal polynomial. Due to the Input Pattern and nonlinear approximation enhancement, the proposed structure can approximate arbitrarily nonlinear decision boundaries. It has been utilized for nonlinear channel equalization. The performance of the proposed adaptive nonlinear equalizer is compared with functional link neural network (FLNN) equalizer, multilayer perceptron (MLP) network and radial basis function (RBF) along with conventional normalized least-mean-square algorithms (NLMS) for different linear and nonlinear channel models. The comparison of convergence rate, bit error rate (BER) and steady state error performance, and computational complexity involved for neural network equalizers is provided.
Haiquan Zhao - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network
Neurocomputing, 2009Co-Authors: Haiquan Zhao, Jiashu ZhangAbstract:A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the Input Pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification. © 2009.
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functional link neural network cascaded with chebyshev orthogonal polynomial for nonlinear channel equalization
Signal Processing, 2008Co-Authors: Haiquan Zhao, Jiashu ZhangAbstract:Nonlinear intersymbol interference (ISI) leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel computationally efficient functional link neural network cascaded with Chebyshev orthogonal polynomial is proposed to combat nonlinear ISI. The equalizer has a simple structure in which the nonlinearity is introduced by functional expansion of the Input Pattern by trigonometric polynomial and Chebyshev orthogonal polynomial. Due to the Input Pattern and nonlinear approximation enhancement, the proposed structure can approximate arbitrarily nonlinear decision boundaries. It has been utilized for nonlinear channel equalization. The performance of the proposed adaptive nonlinear equalizer is compared with functional link neural network (FLNN) equalizer, multilayer perceptron (MLP) network and radial basis function (RBF) along with conventional normalized least-mean-square algorithms (NLMS) for different linear and nonlinear channel models. The comparison of convergence rate, bit error rate (BER) and steady state error performance, and computational complexity involved for neural network equalizers is provided.
Toshihisa Kosaka - One of the best experts on this subject based on the ideXlab platform.
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Rotation-invariant neural Pattern recognition system with application to coin recognition
IEEE Transactions on Neural Networks, 1992Co-Authors: Minoru Fukumi, Sigeru Omatu, Fumiaki Takeda, Toshihisa KosakaAbstract:In Pattern recognition, it is often necessary to deal with problems to classify a transformed Pattern. A neural Pattern recognition system which is insensitive to rotation of Input Pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation Pattern recognition. >
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Rotation-invariant neural Pattern recognition system with application to coin recognition
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991Co-Authors: Minoru Fukumi, Sigeru Omatu, Fumiaki Takeda, Toshihisa KosakaAbstract:The authors propose a Pattern recognition system which is insensitive to the rotation of the Input Pattern by various degrees. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. To illustrate the effectiveness of the system, the authors apply it to rotation-invariant coin recognition of 500 yen and 500 won coins. The results of computer simulation show that a neural network approach will be useful in rotation-invariant Pattern recognition. >
Jagdish C Patra - One of the best experts on this subject based on the ideXlab platform.
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errata to nonlinear dynamic system identification using chebyshev functional link artificial neural networks
Systems Man and Cybernetics, 2002Co-Authors: Jagdish C Patra, Alex C KotAbstract:A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the Input Pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.
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identification of nonlinear dynamic systems using functional link artificial neural networks
Systems Man and Cybernetics, 1999Co-Authors: Jagdish C Patra, Ranendra N Pal, B N Chatterji, G. PandaAbstract:We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the Input Pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.
