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E-raamat: Artificial Neural Networks for Engineering Applications

Edited by (University of Guadalajara, Guadalajara, Jalisco, Mexico), Edited by (University of Guadalajara, Guadalajara, Jalisco, Mexico), Edited by (Dean of Technologies for Cyber-Human Interaction Division (CUCEI), Universidad de Guadalajara, Mexico)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128182482
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Artificial Neural Networks for Engineering ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128182482
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Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications.

  • Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods
  • Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering
  • Contains all the theory required to use the proposed methodologies for different applications
List Of Contributors
v
About The Editors ix
Preface xi
Acknowledgment xiii
1 Hierarchical Dynamic Neural Networks for Cascade System Modeling With Application to Wastewater Treatment
1(8)
Wen Yu
Daniel Carrillo
1.1 Introduction
1(1)
1.2 Cascade Process Modeling Via Hierarchical Dynamic Neural Networks
1(2)
1.3 Stable Training of the Hierarchical Dynamic Neural Networks
3(3)
1.4 Modeling of Wastewater Treatment
6(2)
1.5 Conclusions
8(1)
References
8(1)
2 Hyperellipsoidal Neural Network Trained With Extended Kalman Filter for Forecasting of Time Series
9(12)
Carlos Villasenor
2.1 Introduction
9(1)
2.2 Mathematical Background
9(3)
2.2.1 Mahalanobis Distance
9(1)
2.2.2 Extended Kalman Filter
10(1)
2.2.3 K-Means Online
10(1)
2.2.4 Germinal Center Optimization
11(1)
2.3 HNN for Time Series Forecasting
12(1)
2.4 Results
13(5)
2.4.1 Comparison With the ADALINE Algorithm
13(1)
2.4.2 Experiments With Real-Time Series
14(1)
2.4.3 Mackey-Glass Equation
14(4)
2.5 Conclusion
18(1)
References
18(3)
3 Neural Networks: A Methodology for Modeling and Control Design of Dynamical Systems
21(1)
Fernando Ornelas-Tellez
J. Jesus Rico-Melgoza
Angel E. Villafuerte
Febe J. Zavala-Mendoza
3.1 Introduction
21(1)
3.2 Neural Modeling and Control for Discrete-Time Systems
22(6)
3.2.1 Discrete-Time Uncertain Nonlinear Systems
22(1)
3.2.2 Discrete-Time Recurrent High-Order Neural Network
22(2)
3.2.3 Sliding Mode Block Control Design
24(2)
3.2.4 Application for a Two-Degree of Freedom Robot
26(2)
3.3 Neural Modeling and Control for Continuous-Time Systems
28(5)
3.3.1 Continuous-Time Uncertain Nonlinear Systems
28(1)
3.3.2 Polynomial Neural Identifier
29(1)
3.3.3 Nonlinear Optimal Neural Control Design
30(2)
3.3.4 Application to a Glucose-Insulin System
32(1)
3.4 Further NN Applications
33(2)
3.4.1 Reduced-Order Models
33(1)
3.4.2 Observers Design
34(1)
3.4.3 Prediction
35(1)
3.5 Conclusions
35(1)
References
35(4)
4 Continuous-Time Decentralized Neural Control of a Quadrotor UAV
39(16)
Francisco Jurado
Sergio Lopez
4.1 Introduction
39(2)
4.2 Fundamentals
41(2)
4.2.1 Recurrent High-Order Neural Network (RHONN)
41(1)
4.2.2 Approximation Properties of the RHONN
42(1)
4.2.3 Filtered Error Training Algorithm
43(1)
4.3 Neural Backstepping Controller Design
43(4)
4.4 Results
47(4)
4.5 Conclusion
51(1)
References
52(3)
5 Adaptive PID Controller Using a Multilayer Perceptron Trained With the Extended Kalman Filter for an Unmanned Aerial Vehicle
55(10)
Javier Gomez-Avila
5.1 Introduction
55(1)
5.2 Kalman Filter
55(2)
5.2.7 Extended Kalman Filter
56(1)
5.3 MLP Trained With the EKF
57(1)
5.4 UAV Controlled With an MLP
58(4)
5.4.1 Quadrotor Dynamic Modeling
58(1)
5.4.2 Quadrotor Control Scheme
59(3)
References
62(3)
6 Support Vector Regression for Digital Video Processing
65(14)
Gehova Lopez-Gonzalez
6.1 Introduction
65(3)
6.1.1 Adaptive Filter
65(1)
6.1.2 Frame Interpolation
65(2)
6.1.3 Image Upscaling
67(1)
6.2 Support Vector Regression
68(1)
6.3 SVR Method
69(1)
6.4 Results
70(6)
6.4.1 Image Filtering
70(1)
6.4.2 Filtering, Upscaling, and Motion Regression
71(5)
6.5 Conclusions
76(1)
References
76(3)
7 Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuel Production
79(18)
Kelly J. Gurubel
Edgar N. Sanchez
Rafael Gonzalez
Hugo Coss Y. Leon
Roxana Recio
7.1 Introduction
79(8)
7.2 Activated Sludge Process
87(1)
7.2.1 Activated Sludge Model
81(2)
7.2.2 Discrete-Time RHONO
83(1)
7.2.3 Neural Observer Structure
84(1)
7.2.4 RHONO Performance in the Presence of Disturbances
85(2)
7.3 Anaerobic Digestion Process
87(7)
7.3.1 Two-Stage Anaerobic Digestion Model
87(1)
7.3.2 Neural Identifier
88(1)
7.3.3 NNARX Structures
89(1)
7.3.4 Input-Output Stability Analysis Via Simulation
90(1)
7.3.5 Stability Analysis in the Presence of Disturbances
91(3)
7.4 Conclusion
94(1)
References
95(2)
8 Learning-Based Identification of Viral Infection Dynamics
97(610)
Gustavo Hernandez-Mejia
Esteban A. Hernandez-Vargas
8.1 Introduction
97(1)
8.2 Neural Identification
98(1)
8.2.1 Network Training Based on the Extended Kalman Filter
98(1)
8.3 Within-Host Influenza Infection
98(1)
8.4 Within-Host HIV Infection
99(1)
8.5 Numerical Results
100(4)
8.5.7 IAV-RHONN Identification
100(2)
8.5.2 HIV-RHONN Identification
102(2)
8.6 Conclusions
104(1)
Acknowledgments
104(1)
References
104(3)
9 Attack Detection and Estimation for Cyber-Physical Systems by Using Learning Methodology
107(1)
Haifeng Niu
C. Bhowmick
S. Jagannathan
9.1 Introduction
107(7)
9.2 Background on System Modeling and Attacks
108(1)
9.2.1 Modeling
108(1)
9.2.2 Attack Models
109(1)
9.3 Secure Linear Networked Control Systems
109(6)
9.3.1 Network Attack Detection and Estimation
110(2)
9.3.2 Attack Detection and Estimation in Linear Physical System
112(3)
9.4 Secure Nonlinear Networked Control Systems
115(5)
9.4.1 Nonlinear Network Attack Detection and Estimation
115(2)
9.4.2 Attack Detection and Estimation in Nonlinear Physical Systems
117(3)
9.5 Results and Discussion
120(5)
9.5.1 Simulation Results for Linear NCS
120(2)
9.5.2 Simulation Results for Nonlinear NCS
122(3)
9.6 Conclusions
125(1)
References
125(2)
10 Sensitivity Analysis With Artificial Neural Networks for Operation of Photovoltaic Systems
127(12)
O. May Tzuc
A. Bassam
L.J. Ricalde
E. Cruz May
10.1 Introduction
127(1)
10.2 Experimental Facility and Database
128(1)
10.3 Sensitivity Analysis
128(5)
10.3.1 Sensitivity Analysis Classification
129(2)
10.3.2 Elementary Effect Test
131(1)
10.3.3 Elementary Effect Test Visualization
132(1)
10.4 Application
133(4)
10.4.1 Modeling and Sensitivity Analysis Workflow
133(1)
10.4.2 Artificial Neural Network
134(1)
10.4.3 Sensitivity Analysis Results
134(3)
10.5 Conclusions
137(1)
References
137(2)
11 Pattern Classification and Its Applications to Control of Biomechatronic Systems
139(16)
Victor H. Benitez
11.1 Introduction
139(1)
11.2 Biomechatronic System Components
139(4)
11.2.1 Biological System
139(1)
11.2.2 Surface Electrodes
140(1)
11.2.3 Signal Conditioning
140(1)
11.2.4 Signal Processing
140(3)
11.2.5 Controller Deployment and User Application
143(1)
11.2.6 Neural Network Control
143(1)
11.3 Biomechatronic System Proposed
143(10)
11.3.1 Problem Formulation
144(1)
11.3.2 Experimental Preparation
145(1)
11.3.3 Experimental Set-Up
145(2)
11.3.4 Signal Processing
147(1)
11.3.5 Control of Biomechatronic System
148(5)
11.4 Conclusion
153(1)
References
153(2)
Index 155
Dr. Alma Y. Alanis received her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara, Mexico. Since 2008 she has been with University of Guadalajara, where she is currently a Dean of the Technologies for Cyber-Human Interaction Division, CUCEI. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides eight international books. Dr. Alanis is a Senior Member of the IEEE and Subject Editor of the Journal of Franklin Institute, Section Editor at Open Franklin, Technical Editor at ASME/IEEE Transactions on Mechatronics, and Associate Editor at IEEE Transactions on Cybernetics, Intelligent Automation & Soft Computing and Engeenering Applications of Artifical Intelligence. Moreover, Dr. Alanis is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 Dr. Alanis received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Her research interest centers on artificial neural networks, learning systems, intelligent control, and intelligent systems. Nancy Arana-Daniel received her B. Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer sicence from Research Center of the National Polytechnic Institute and Advanced Studies, CINVESTAV, in 2003 and 2007 respectively. She is currently a research fellow at the University of Guadalajara, in the Department of Computer Science Mxico, where she is working at the Laboratory of Intelligent Systems and the Research Center for Control Systems and Artificial Intelligence. She is IEEE Senior member and a member of National System of Researchers (SNI-1). She has published several papers in International Journals and Conferences and she has been technical manager of several projects that have been granted by the Nacional Council of Science and Technology (CONACYT). Also, se has collaborated in an international project granted by OPTREAT), She is Associated Editor of the Journal of Franklin Institute (Elsevier). Her research interests focus on applications of geometric algebra, geometric computing, machine learning, bio-inspired optimization, pattern recognition and robot navigation. Carlos Lpez-Franco received the Ph.D. degree in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV, Mexico. He is currently a professor at the University of Guadalajara, Mexico, Computer Science Department, and member of the Intelligent Systems group. He is IEEE Senior member and a member of National System of Researchers) or SNI, level 1. His research interests include geometric algebra, computer vision, robotics and intelligent systems.
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