scholarly journals A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 62 ◽  
Author(s):  
Alexander Vlasenko ◽  
Nataliia Vlasenko ◽  
Olena Vynokurova ◽  
Dmytro Peleshko
Keyword(s):  
Time Series ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Fuzzy Model ◽  
Nonlinear Behavior ◽  
Additional Information ◽  
Computational Performance ◽  
Neuro Fuzzy ◽  
Competing Models

Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

scholarly journals Application of particle swarm optimization with ANFIS model for double scroll chaotic system

2021 ◽  
Vol 11 (1) ◽  
pp. 328
Author(s):  
W. A. Wali
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Anfis Model ◽  
Inference System ◽  
Nonlinear Resistor ◽  
Additional Information ◽  
Neuro Fuzzy ◽  
Chaotic Time Series Prediction

The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

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