Delaunay input space partitioning modelling

2002 ◽  
pp. 201-224
Author(s):  
Chris Harris ◽  
Xia Hong ◽  
Qiang Gan
Keyword(s):  
Space Partitioning ◽  
Input Space

scholarly journals COMPUTATIONAL INTELLIGENCE METHODS FOR FINANCIAL TIME SERIES MODELING

2006 ◽  
Vol 16 (07) ◽  
pp. 2053-2062 ◽  
Author(s):  
N. G. PAVLIDIS ◽  
D. K. TASOULIS ◽  
V. P. PLAGIANAKOS ◽  
M. N. VRAHATIS
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Real Life ◽  
Feedforward Neural Networks ◽  
Unsupervised Clustering ◽  
Space Partitioning ◽  
Input Space

In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in real-life applications. An improvement in the one-step-ahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar.

scholarly journals Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 700 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Performance Comparison ◽  
Combined Cycle ◽  
Subtractive Clustering ◽  
Segmentation Method ◽  
Clustering Method ◽  
Space Partitioning ◽  
Anfis Model ◽  
Inference System ◽  
Input Space ◽  
Fuzzy C Means

In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

Input Space Partitioning to Enable Massively Parallel Proof

2017 ◽  
pp. 139-145 ◽  
Author(s):  
Ashlie B. Hocking ◽  
M. Anthony Aiello ◽  
John C. Knight ◽  
Nikos Aréchiga
Keyword(s):  
Massively Parallel ◽  
Space Partitioning ◽  
Input Space

Input Space Partitioning

2012 ◽  
pp. 150-169
Author(s):  
Paul Ammann ◽  
Jeff Offutt
Keyword(s):  
Space Partitioning ◽  
Input Space

Input Space Partitioning for Neural Network Learning

2013 ◽  
Vol 4 (2) ◽  
pp. 56-66 ◽  
Author(s):  
Shujuan Guo ◽  
Sheng-Uei Guan ◽  
Weifan Li ◽  
Ka Lok Man ◽  
Fei Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Results ◽  
Ensemble Method ◽  
Learning Performance ◽  
Space Partitioning ◽  
Classification Result ◽  
Neural Network Learning ◽  
Input Space ◽  
Network Learning

To improve the learning performance of neural network (NN), this paper introduces an input attribute grouping based NN ensemble method. All of the input attributes are partitioned into exclusive groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive interactions between attributes. After partitioning, multiple NNs are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each NN. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.

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