scholarly journals Supervised neural networks learning algorithm for three dimensional hybrid nanofluid flow with radiative heat and mass fluxes

2021 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Muhammad Shoaib ◽  
Zeeshan Khan ◽  
Samina Zuhra ◽  
C. Ahamed Saleel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Radiative Heat ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Hybrid Nanofluid ◽  
Nanofluid Flow ◽  
Mass Fluxes

Three-Dimensional Hybrid Nanofluid Flow and Heat Transfer past a Permeable Stretching/Shrinking Sheet with Velocity Slip and Convective Condition

2020 ◽  
Vol 66 ◽  
pp. 157-171 ◽  
Author(s):  
Najiyah Safwa Khashi'ie ◽  
Norihan Md Arifin ◽  
Ioan Pop ◽  
Roslinda Nazar ◽  
Ezad Hafidz Hafidzuddin ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Three Dimensional ◽  
Velocity Slip ◽  
Shrinking Sheet ◽  
Hybrid Nanofluid ◽  
Convective Condition ◽  
Nanofluid Flow ◽  
Flow And Heat Transfer

NEURAL ADAPTIVE CONTROL OF NONLINEAR MULTIVARIABLE SYSTEMS WITH APPLICATION TO A CLASS OF INVERTED PENDULUMS

2002 ◽  
Vol 12 (05) ◽  
pp. 411-424
Author(s):  
SHOULING HE
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Base Point ◽  
Upper Body ◽  
Model Parameters ◽  
State Variables

In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots.

Active Noise Control Using Multi-Layered Perceptron Neural Networks

1997 ◽  
Vol 16 (2) ◽  
pp. 109-144 ◽  
Author(s):  
M.O. Tokhi ◽  
R. Wood
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Noise Control ◽  
Learning Algorithm ◽  
Free Field ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Broadband Noise ◽  
Active Noise

This paper presents the development of a neuro-adaptive active noise control (ANC) system. Multi-layered perceptron neural networks with a backpropagation learning algorithm are considered in both the modelling and control contexts. The capabilities of the neural network in modelling dynamical systems are investigated. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment and simulation results verifying its performance are presented and discussed.

scholarly journals Three Dimensional MHD Hybrid Nanofluid Flow with Rotating Stretching/Shrinking Sheet and Joule Heating

CFD letters ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1-19
Author(s):  
Yuan Ying Teh ◽  
Adnan Ashgar
Keyword(s):  
Boundary Layer Thickness ◽  
Stretching Sheet ◽  
Magnetic Parameter ◽  
Volume Fraction ◽  
Three Dimensional ◽  
Shrinking Sheet ◽  
Hybrid Nanofluid ◽  
Cu Nanoparticles ◽  
Nanofluid Flow ◽  
Nanoparticles Volume Fraction

A three-dimensional hybrid nanofluid flow over a stretching/shrinking sheet is numerically studied. The hybrid nanofluid being considered in this study used water as the base fluid and mixed with two types of solid nanoparticles, namely alumina (Al2O3) and copper (Cu). The main focus of the current study is to examine the effect of magnetic field, Joule heating, and rotating sheet on the velocity, and temperature profiles. In addition, the impact of suction and stretching sheet on the variations of reduced skin friction, , and reduced heat transfer are studied as well. The fluid flow and heat transfer problem presented in this study is governed by a system of nonlinear partial differential equations (PDEs), which is then transformed into the corresponding system of high order nonlinear ordinary differential equations (ODEs) using similarity variables. The resulting system of higher order nonlinear ODEs is solved numerically using a boundary value solver known as bvp4c, which operates on the MATLAB computational platform. Results revealed that dual solutions exist for shrinking sheet while unique solutions are observed for stretching sheet with various values of Cu nanoparticles volume fraction and magnetic parameter. Dual solutions also exist for the value of the suction parameter greater than its critical point with various values of Cu nanoparticles volume fraction. Velocity profile of the hybrid nanofluid increases alongside with the value of magnetic parameter but declination was observed in the profile of and temperature, for both solutions as the value of Cu nanoparticles volume fraction increases. When the value of rotational parameter increases, both velocity and profiles increase for both solutions. This indicates that the momentum boundary layer thickness increases with increasing values of for both solutions, but thermal boundary layer thickness decreases for the first solution and increases for the second solution. Finally, an increment in the value of Eckert number causes the temperature of the hybrid nanofluid to rise as well for both first and second solutions.

Impact of autocatalysis chemical reaction on nonlinear radiative heat transfer of unsteady three-dimensional Eyring–Powell magneto-nanofluid flow

Pramana ◽  
2018 ◽  
Vol 91 (5) ◽  
Author(s):  
W A Khan ◽  
A S Alshomrani ◽  
A K Alzahrani ◽  
M Khan ◽  
M Irfan
Keyword(s):  
Heat Transfer ◽  
Chemical Reaction ◽  
Radiative Heat Transfer ◽  
Radiative Heat ◽  
Three Dimensional ◽  
Nanofluid Flow

scholarly journals Three dimensional MHD upper-convected Maxwell nanofluid flow with nonlinear radiative heat flux

2018 ◽  
Vol 57 (3) ◽  
pp. 1917-1925 ◽  
Author(s):  
M. Bilal ◽  
M. Sagheer ◽  
S. Hussain
Keyword(s):  
Heat Flux ◽  
Radiative Heat ◽  
Three Dimensional ◽  
Radiative Heat Flux ◽  
Nanofluid Flow ◽  
Maxwell Nanofluid ◽  
Nonlinear Radiative Heat Flux

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01048
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
Deep Learning Algorithm ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks (CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-the-art of Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01003
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

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