Modular Neural Network Preprocessing Procedure with Intuitionistic Fuzzy InterCriteria Analysis Method

2015 ◽  
pp. 175-186 ◽  
Author(s):  
Sotir Sotirov ◽  
Evdokia Sotirova ◽  
Patricia Melin ◽  
Oscar Castilo ◽  
Krassimir Atanassov
Keyword(s):  
Neural Network ◽  
Analysis Method ◽  
Modular Neural Network ◽  
Intuitionistic Fuzzy

scholarly journals Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Sotir Sotirov ◽  
Vassia Atanassova ◽  
Evdokia Sotirova ◽  
Lyubka Doukovska ◽  
Veselina Bureva ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hardware Implementation ◽  
Data Preprocessing ◽  
Yield Reduction ◽  
Analysis Method ◽  
Intuitionistic Fuzzy ◽  
Input Parameters ◽  
The Neural Networks ◽  
First Time

The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network’s processing of data and images.

Design and evolution of modular neural network architectures

1994 ◽  
Vol 7 (6-7) ◽  
pp. 985-1004 ◽  
Author(s):  
Bart L.M. Happel ◽  
Jacob M.J. Murre
Keyword(s):  
Neural Network ◽  
Network Architectures ◽  
Modular Neural Network ◽  
Neural Network Architectures

Predication of Displacement of Tunnel Rock Mass Based on the Back-Analysis Method-BP Neural Network

2021 ◽  
Author(s):  
Wenzheng Cao ◽  
Yujing Jiang ◽  
Osamu Sakaguchi ◽  
Ningbo Li ◽  
Wei Han
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Analysis Method

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

A modular neural network architecture for inverse kinematics model learning

2001 ◽  
Vol 38-40 ◽  
pp. 797-805 ◽  
Author(s):  
Eimei Oyama ◽  
Arvin Agah ◽  
Karl F. MacDorman ◽  
Taro Maeda ◽  
Susumu Tachi
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Inverse Kinematics ◽  
Neural Network Architecture ◽  
Model Learning ◽  
Kinematics Model ◽  
Modular Neural Network
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