Forecasting chaotic time series using neuro-fuzzy approach

Palit, A.K.; Popović, D.

doi:10.1109/ijcnn.1999.832598

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…The structure of Takagi-Sugeno Neuro-Fuzzy we use here is the same with that one described in [4,5] which is similar with MANFIS (Multiple-output ANFIS) model [1,6]. For convenient, here we redraw the neuro-fuzzy model proposed by [5] in comparison with MANFIS model [1], both for two input and two output system.…”

Section: Mimo Takagi-sugeno Neuro-fuzzy For Forecasting Chaotic Time mentioning

confidence: 99%

“…But, Takagi-Sugeno model is preferred in the case of data-based (or numerically-data-driven) fuzzy modeling [1,2,3] as well as forecasting time series data where in many cases it can be seen as system with locally linear model. Another advantage is that the inference formula of the Takagi-Sugeno model is only two-step procedure, based on a weighted average defuzzifier, whereas Mamdani type of fuzzy model basically consists of four steps [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Here we choose TakagiSugeno type fuzzy model along with differentiable operators and membership function and also the weighted average defuzzifier for defuzzification of output data. The corresponding output inference can then be represented in a multilayer feedforward network structure such as described in [4,5] which is identical to the architecture of ANFIS [6]. Since we use Takagi-Sugeno fuzzy model with Gaussian Membership Function (GMF), then we have three types of free parameters: mean and variance of each GMFs and also Takagi-Sugeno Rule's Consequent Parameters (we usually name them as theta).…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation using Least Square Method for MIMO Takagi-Sugeno Neuro-Fuzzy in Time Series Forecasting

Sugiarto¹,

Natarajan²

2008

jte

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This paper describes LSE method for improving Takagi-Sugeno neuro-fuzzy model for a multi-input and multi-output system using a set of data (Mackey-Glass chaotic time series). The performance of the generated model is verified using certain set of validation / test data. The LSE method is used to compute the consequent parameters of Takagi-Sugeno neurofuzzy model while mean and variance of Gaussian Membership Functions are initially set at certain values and will be updated using Back Propagation Algorithm. The simulation using Matlab shows that the developed neuro-fuzzy model is capable of forecasting the future values of the chaotic time series and adaptively reduces the amount of error during its training and validation.

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Section: Mimo Takagi-sugeno Neuro-fuzzy For Forecasting Chaotic Time mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation using Least Square Method for MIMO Takagi-Sugeno Neuro-Fuzzy in Time Series Forecasting

Sugiarto¹,

Natarajan²

2008

jte

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“…Palit and Popovic (1999, 2000 applied the neuro fuzzy network for time series forecasts. Kisi (2005) estimated suspended sediment using neuro-fuzzy and neural network approaches.…”

mentioning

confidence: 99%

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Kisi

Shiri

2011

Water Resour Manage

152

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Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In the present study the abilities of hybrid wavelet-genetic programming [i.e. wavelet-gene-expression programming, WGEP] and wavelet-neuro-fuzzy (WNF) models for daily precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neurofuzzy (NF) models are applied to forecast daily precipitation amounts based on previously recorded values, but the results are very weak. In the next step the hybrid WGEP and WNF models are used by introducing the wavelet coefficients as GEP and NF inputs, but no satisfactory results are produced, even though the accuracies increased to a great extent. In the third step, the new WGEP and WNF models are built; by merging the best single and hybrid models' inputs and introducing them as the models inputs. The results show the new hybrid WGEP models are effective in forecasting daily precipitation, while the new WNF models are unable to learn the non linear process of precipitation very well.

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“…2, 3,.., n; are the n number of inputs to the system, whereas, , with j= 1, 2, 3 ._., m; are the m number of outputs from the system, and Gil, with, i= 1. 2,3 ,.., n, and 1 = 1. 2, 3. .., M , are the Gaussian membership functions (GMFs) with corresponding mean and variance parameters as c;, of respectively and y'.…”

mentioning

confidence: 99%

Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

Palit¹,

Doeding²,

Anheier³

et al.

2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proc

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The paper describes a Backpropagation based algorithm that can be used to train the Takagi-Sugeno (TS) type multi-input mnlti-ontput (MIMO) neuro-fuzzy network elfciently. m e training algorithm is elfcient in the sense that it can bring the performance index of the nehvorb such as the sum squared error (SSE), down to the desired error goal much faster than that the simple backpropagation algorithm (BPA). Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of Electrical load time series.

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Forecasting chaotic time series using neuro-fuzzy approach

Cited by 9 publications

References 2 publications

Parameter Estimation using Least Square Method for MIMO Takagi-Sugeno Neuro-Fuzzy in Time Series Forecasting

Parameter Estimation using Least Square Method for MIMO Takagi-Sugeno Neuro-Fuzzy in Time Series Forecasting

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

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