Fuzzy Neural Network Design using Support Vector Regression for Function Approximation with Outliers

2006 ◽  
Author(s):  
Chin-Teng Lin ◽  
Sheng-Fu Liang ◽  
Chang-Moun Yeh ◽  
Kan Wei Fan
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Support Vector Regression ◽  
Function Approximation ◽  
Fuzzy Neural Network ◽  
Support Vector ◽  
Fuzzy Neural

scholarly journals HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX

2018 ◽  
Vol 24 (6) ◽  
pp. 2161-2178 ◽  
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver ◽  
Rima Tamošiūnienė
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Stock Index ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Accurate Information ◽  
Investment Strategies ◽  
Trend Prediction ◽  
Fuzzy Neural ◽  
Input Variables

Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.

scholarly journals Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

2021 ◽  
Vol 14 ◽  
Author(s):  
Daewon Park ◽  
Tien-Loc Le ◽  
Nguyen Vu Quynh ◽  
Ngo Kim Long ◽  
Sung Kyung Hong
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Attitude Control ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Learning Ability ◽  
System Stability ◽  
Effective Control ◽  
Fuzzy Neural ◽  
Online Tuning

This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

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