scholarly journals Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

2020 ◽  
Vol 28 (5) ◽  
pp. 1003-1015 ◽  
Author(s):  
Dongrui Wu ◽  
Ye Yuan ◽  
Jian Huang ◽  
Yihua Tan
Keyword(s):  
Fuzzy Systems ◽  
Gradient Descent ◽  
Regression Problems

scholarly journals Design of a hierarchical fuzzy model predictive controller

2015 ◽  
Vol 4 (2) ◽  
pp. 342 ◽  
Author(s):  
Zeinab Fallah ◽  
Mojtaba Ahmadieh Khanesar ◽  
Mohammad Teshnehlab
Keyword(s):  
Nonlinear Model ◽  
Fuzzy Systems ◽  
Gradient Descent ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Least Square ◽  
Continuous Stirred Tank Reactor ◽  
Descent Algorithm ◽  
Neuro Fuzzy ◽  
Gradient Descent Algorithm

In order to control a nonlinear system using Nonlinear Model Predictive Control (NMPC), a nonlinear model from system is required. In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant. The use of hierarchical neuro-fuzzy systems makes it possible to overcome the curse of dimensionality. In neuro-fuzzy systems, if the input number increases, then the number of rules increases exponentially. One solution to this problem is making use of Hierarchical Fuzzy System Mamdani (HFS) in which the number of the rules increases linearly. Gradient descent and recursive least square algorithm are used simultaneously to train the parameters of the HFS. Gradient Descent Algorithm is utilized to train the parameters, which appear nonlinearly in the output of HFS, and RLS is used to train the parameters of consequent the part, which appears linearly in the output of HFS. Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized. The proposed controller is simulated on the control of continuous stirred tank reactor. It is shown that the proposed method can control the system with high performance.

scholarly journals The Power Cepstrum Calculation with Convolutional Neural Networks

2019 ◽  
Vol 19 (2) ◽  
pp. e13 ◽  
Author(s):  
Mario Alejandro García ◽  
Eduardo Atilio Destéfanis
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Gradient Descent ◽  
Descent Method ◽  
Intermediate Step ◽  
Gradient Descent Method ◽  
The Neural Network ◽  
Regression Problems ◽  
The One ◽  
Deep Learning Model

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.

scholarly journals BIBLIOMETRIC MAPPING OF THE COMPUTATIONAL INTELLIGENCE FIELD

2007 ◽  
Vol 15 (05) ◽  
pp. 625-645 ◽  
Author(s):  
NEES JAN VAN ECK ◽  
LUDO WALTMAN
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Optimization Problems ◽  
Control Problems ◽  
Classification Problems ◽  
Current Structure ◽  
The Neural Networks ◽  
Regression Problems

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

H∞ control for Takagi–Sugeno fuzzy systems

2001 ◽  
Vol 32 (7) ◽  
pp. 915-924 ◽  
Author(s):  
Jun Yoneyama ◽  
Masahiro Nishikawa ◽  
Hitoshi Katayama ◽  
Akira Ichikawa
Keyword(s):  
Fuzzy Systems ◽  
Takagi Sugeno

Improvement of AODV Routing on MANETs Using Fuzzy Systems

2011 ◽  
Vol 7 (2) ◽  
pp. 102-106 ◽  
Author(s):  
Taqwa Odey Fahad ◽  
Abduladhim A. Ali
Keyword(s):  
Fuzzy Systems
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