scholarly journals Artificial Neural Networks Models Based on ARX and State Space Forms and Adaptive Control PID/LQR of Systems Based on Peltier Cells

2021 ◽  
Vol 9 (11) ◽  
pp. 455-477
Author(s):  
Denis Fabricio Sousa De Sá ◽  
João Viana Fonseca Neto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Strategies ◽  
Least Square ◽  
Parametric Estimation ◽  
Training Algorithms ◽  
Recursive Least Square ◽  
Space Forms ◽  
Linear Regulator ◽  
Artificial Neural

To improve the performance of a thermal plant based on Peltier cell actuators, an online parametric estimation via artificial neural networks and adaptive controller is presented. The control actions  are based on adaptive digital controller and an adaptive quadratic linear regulator approaches. The Artificial neural networks topology is based on ARX and NARX models, and its training algorithms, such as accelerated backpropagation and recursive least square. The Control strategies are design-oriented to adaptive digital PID controller and quadratic linear regulator framework. The proposal is evaluated on  temperature control of an object that is inside of a chamber.

scholarly journals Conventional and Fuzzy Force Control in Robotised Machining

2013 ◽  
Vol 210 ◽  
pp. 178-185 ◽  
Author(s):  
Zenon Hendzel ◽  
Andrzej Burghardt ◽  
Piotr Gierlak ◽  
Marcin Szuster
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Force Control ◽  
Closed Loop ◽  
Position Control ◽  
Control Strategies ◽  
Closed Loop System ◽  
Fuzzy Logic Systems ◽  
Complex Control System ◽  
Artificial Neural

This article presents an application of the hybrid position-force control of the robotic manipulator with use of artificial neural networks and fuzzy logic systems in complex control system. The mathematical description of the manipulator and a closed-loop system are presented. In the position control were used the PD controller and artificial neural networks, which compensate nonlinearities of the manipulator. The paper presents mainly the application of various strategies of the force control. The force control strategies using conventional controllers P, PI, PD, PID and fuzzy controllers are presented and discussed. All of the control methods were verified on the real object in order to make a comparison of a control quality.

scholarly journals Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
E. Chatzimichail ◽  
E. Paraskakis ◽  
A. Rigas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Decision Making ◽  
Persistent Asthma ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Asthma Outcome ◽  
Artificial Neural

The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood.

Higher Order Neural Networks for Stock Index Modeling

2009 ◽  
pp. 113-131 ◽  
Author(s):  
Yuehui Chen ◽  
Peng Wu ◽  
Qiang Wu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Higher Order ◽  
Stock Index ◽  
Training Algorithms ◽  
Window Method ◽  
Higher Order Neural Networks ◽  
Artificial Neural ◽  
Stock Index Forecasting

Artificial Neural Networks (ANNs) have become very important in making stock market predictions. Much research on the applications of ANNs has proven their advantages over statistical and other methods. In order to identify the main benefits and limitations of previous methods in ANNs applications, a comparative analysis of selected applications is conducted. It can be concluded from analysis that ANNs and HONNs are most implemented in forecasting stock prices and stock modeling. The aim of this chapter is to study higher order artificial neural networks for stock index modeling problems. New network architectures and their corresponding training algorithms are discussed. These structures demonstrate their processing capabilities over traditional ANNs architectures with a reduction in the number of processing elements. In this chapter, the performance of classical neural networks and higher order neural networks for stock index forecasting is evaluated. We will highlight a novel slide-window method for data forecasting. With each slide of the observed data, the model can adjusts the variable dynamically. Simulation results show the feasibility and effectiveness of the proposed methods.

Visualization of Neuro-Fuzzy Networks Training Algorithms

2016 ◽  
pp. 222-257 ◽  
Author(s):  
Antonia Plerou ◽  
Elena Vlamou ◽  
Basil Papadopoulos
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Adaptive Systems ◽  
Automatic Identification ◽  
Training Algorithms ◽  
Fuzzy Logic Systems ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real world problems through the development of intelligent and adaptive systems. Artificial Neural networks are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based signals for training. Ultimately this process may be employed for automatic identification of patterns introduced in medical applications and more specifically for analysis of content produced by brain imaging processes.

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

2021 ◽  
Vol 63 (6) ◽  
pp. 565-570
Author(s):  
Serkan Balli ◽  
Faruk Sen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Modes ◽  
Experimental Studies ◽  
Composite Plates ◽  
Geometric Parameters ◽  
Sandwich Composite ◽  
Geometrical Parameters ◽  
Training Algorithms ◽  
Artificial Neural

Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images

2020 ◽  
Vol 17 (4) ◽  
pp. 1831-1838
Author(s):  
T. Chithambaram ◽  
K. Perumal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Brain Tumor ◽  
Back Propagation ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Training Algorithms ◽  
Back Propagation Algorithm ◽  
Brain Tumor Segmentation ◽  
Artificial Neural

Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.

scholarly journals Adaptive Neural Motion Control of a Quadrotor UAV

Vehicles ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 468-490
Author(s):  
Hugo Yañez-Badillo ◽  
Ruben Tapia-Olvera ◽  
Francisco Beltran-Carbajal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Motion Control ◽  
Current Knowledge ◽  
Control Strategies ◽  
Trajectory Tracking Control ◽  
B Spline ◽  
Research Fields ◽  
On Line ◽  
Artificial Neural

Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities.

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