The Experts below are selected from a list of 139419 Experts worldwide ranked by ideXlab platform
Garba Inoussa - One of the best experts on this subject based on the ideXlab platform.
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nonlineAR time series Modeling and prediction using functional weights wavelet neural network based state dependent AR Model
Neurocomputing, 2012Co-Authors: Garba Inoussa, Hui PengAbstract:This paper presents a Functional Weights Wavelet Neural Network-based state-dependent AR (FWWNN-AR) Model with the main objective to address the Modeling and prediction problem of nonlineAR time series. The FWWNN-AR Model is a state-dependent autoregressive (SD-AR) Model, which has its coefficients approximated by a set of Functional Weights Wavelet Neural Network (FWWNN). The FWWNN is an enhanced type of wavelet neural network comprising of five layers: input, wavelet, product, output and functional weight layer that computes the weights as function of inputs thus making the weights to vARy with the inputs and to shARe the dynamics with the wavelet compARtment. The FWWNN-AR Model possesses both the advantages of the state-dependent AR Model in the description of nonlineAR dynamics using few nodes and of the FWWNN in functional approximation considering mutually the time and frequency spaces. It leARns the nonlineAR dynamics from three distinct levels: AR level, Wavelet compARtment level and functional weights level. A Structured NonlineAR PARameter Optimization Method (SNPOM) is applied to estimate the FWWNN-AR Model pARameters. This leARning approach divides the pARameter seARch space into lineAR and nonlineAR subspaces and centers the seARch in the nonlineAR subspace, but at each iteration in the optimization process, a seARch in the nonlineAR (or lineAR) subspace is executed on the basis of the estimated values just obtained in lineAR (or nonlineAR) subspace. The seARch in the nonlineAR subspace uses a method similAR to the Levemberg-MARquARdt Method (LMM), and the seARch in the lineAR subspace uses the Least SquARe Method (LSM). The proposed Model is validated by compARing its performances and effectiveness with those achieved by some well known Models on both generated and real nonlineAR time series.
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a locally lineAR rbf network based state dependent AR Model for nonlineAR time series Modeling
Information Sciences, 2010Co-Authors: Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen, Garba InoussaAbstract:This paper presents a Modeling approach to nonlineAR time series that uses a set of locally lineAR radial basis function networks (LLRBFNs) to approximate the functional coefficients of the state-dependent autoregressive (SD-AR) Model. The resulting Model, called the locally lineAR radial basis function network-based autoregressive (LLRBF-AR) Model, combines the advantages of the LLRBFN in function approximation and of the SD-AR Model in nonlineAR dynamics description. The LLRBFN weights that connect the hidden units with the output ARe lineAR functions of the input vARiables; this differs from the conventional RBF network weight structure. A structured nonlineAR pARameter optimization method (SNPOM) is applied to estimate the LLRBF-AR Model pARameters. Case studies on vARious time series and chaotic systems show that the LLRBF-AR Modeling approach exhibits much better prediction accuracy compARed to some other existing methods.
Hui Peng - One of the best experts on this subject based on the ideXlab platform.
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nonlineAR time series Modeling and prediction using functional weights wavelet neural network based state dependent AR Model
Neurocomputing, 2012Co-Authors: Garba Inoussa, Hui PengAbstract:This paper presents a Functional Weights Wavelet Neural Network-based state-dependent AR (FWWNN-AR) Model with the main objective to address the Modeling and prediction problem of nonlineAR time series. The FWWNN-AR Model is a state-dependent autoregressive (SD-AR) Model, which has its coefficients approximated by a set of Functional Weights Wavelet Neural Network (FWWNN). The FWWNN is an enhanced type of wavelet neural network comprising of five layers: input, wavelet, product, output and functional weight layer that computes the weights as function of inputs thus making the weights to vARy with the inputs and to shARe the dynamics with the wavelet compARtment. The FWWNN-AR Model possesses both the advantages of the state-dependent AR Model in the description of nonlineAR dynamics using few nodes and of the FWWNN in functional approximation considering mutually the time and frequency spaces. It leARns the nonlineAR dynamics from three distinct levels: AR level, Wavelet compARtment level and functional weights level. A Structured NonlineAR PARameter Optimization Method (SNPOM) is applied to estimate the FWWNN-AR Model pARameters. This leARning approach divides the pARameter seARch space into lineAR and nonlineAR subspaces and centers the seARch in the nonlineAR subspace, but at each iteration in the optimization process, a seARch in the nonlineAR (or lineAR) subspace is executed on the basis of the estimated values just obtained in lineAR (or nonlineAR) subspace. The seARch in the nonlineAR subspace uses a method similAR to the Levemberg-MARquARdt Method (LMM), and the seARch in the lineAR subspace uses the Least SquARe Method (LSM). The proposed Model is validated by compARing its performances and effectiveness with those achieved by some well known Models on both generated and real nonlineAR time series.
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a locally lineAR rbf network based state dependent AR Model for nonlineAR time series Modeling
Information Sciences, 2010Co-Authors: Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen, Garba InoussaAbstract:This paper presents a Modeling approach to nonlineAR time series that uses a set of locally lineAR radial basis function networks (LLRBFNs) to approximate the functional coefficients of the state-dependent autoregressive (SD-AR) Model. The resulting Model, called the locally lineAR radial basis function network-based autoregressive (LLRBF-AR) Model, combines the advantages of the LLRBFN in function approximation and of the SD-AR Model in nonlineAR dynamics description. The LLRBFN weights that connect the hidden units with the output ARe lineAR functions of the input vARiables; this differs from the conventional RBF network weight structure. A structured nonlineAR pARameter optimization method (SNPOM) is applied to estimate the LLRBF-AR Model pARameters. Case studies on vARious time series and chaotic systems show that the LLRBF-AR Modeling approach exhibits much better prediction accuracy compARed to some other existing methods.
Dusan Marcek - One of the best experts on this subject based on the ideXlab platform.
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stock price forecasting statistical classical and fuzzy neural network approach
Lecture Notes in Computer Science, 2004Co-Authors: Dusan MarcekAbstract:An AR Model, a classical neural feedforwARd network and an ARtificial fuzzy neural network based on B-spline member ship functions ARe presented and considered. Some preliminARy results and further experiments that we performed ARe presented.
Amir Akramin Shafie - One of the best experts on this subject based on the ideXlab platform.
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ARtificial neural network based autoregressive Modeling technique with application in voice activity detection
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Abiodun Musa Aibinu, Momoh Jimoh Emiyoka Salami, Amir Akramin ShafieAbstract:A new method of estimating the coefficients of an autoregressive (AR) Model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR Model ARe obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech.
Gregory C Reinsel - One of the best experts on this subject based on the ideXlab platform.
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vector autoregressive Models with unit roots and reduced rank structure estimation likelihood ratio test and forecasting
Journal of Time Series Analysis, 1992Co-Authors: Gregory C ReinselAbstract:. The nonstationARy multivARiate autoregressive (AR) Model Φ (L)Yt=et is considered for an m-dimensional process {Yt}, where it is assumed that det {Φ(L)}= 0 has d< m unit roots and all other roots ARe outside the unit circle, and also that rank {Φ(1)}=r (r=m–d). Limiting distribution results obtained by Ahn and Reinsel for the least-squARes and the Gaussian reduced rank (unit roots imposed) estimators for this AR Model ARe extended to a Model where the AR pARameters possess additional structure such as nested reduced rank, and based on these results the asymptotic distribution of the likelihood ratio test statistic for testing the number d of unit roots is obtained. An analysis of three US monthly interest rate series is presented to illustrate the testing and estimation procedures. A small simulation study is also performed to examine the finite-sample properties of the likelihood ratio test and the prediction performance of Models which impose different numbers of unit roots.