neural networks for instrumentation, measurement and related industrial applications

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neural networks for instrumentation, measurement and related industrial applications

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[...]... Siegel Instrumentation and measurement systems for robotics: issues, problems, and techniques Neural network techniques for instrumentation, measurement systems, and robotic applications: theory, design, and practical issues Case studies: neural networks for instrumentation and measurement systems in robotic applications in research and industry Neural Networks for Measurement and Instrumentation in... networks to the medical field Index Author Index 291 291 291 299 312 323 329 Neural Networks for Instrumentation, Measurement and Related Industrial Applications S Ablameyko et al (Eds.) IOS Press, 2003 Chapter 1 Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications Vincenzo PIURI Department of Information Technologies, University of Milan via Bramante 65, 26013 Crema,... to the Expression of Uncertainty in Measurement, 1993 This page intentionally left blank Neural Networks for Instrumentation, Measurement and Related Industrial Applications S Ablameyko et al (Eds.) IOS Press, 2003 19 Chapter 3 Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications Stefano FERRARI, Vincenzo PIURI Department of Information Technologies, University... computing methodologies for intelligent measurement systems Industrial applications of soft sensors and neural measurement systems Neural Networks for Measurement and Instrumentation in Virtual Environments, Emil M Petriu Introduction Modeling natural objects, processes, and behaviors for real-time virtual environment applications Hardware NN architectures for real-time modeling applications Case study:... be considered in order to enhance and expand the benefits provided by higher training in the topics of this book NIMIA'2001 was the starting point that allowed for coordinating, homogenizing, and consolidating educational efforts on neural technologies for V Piuri and S Ablameyko / Introduction to Neural Networks 1 instrumentation, measurement, and related industrial applications This book, conference... Cesare Alippi and Anthony Blom Introduction Equipment and instrumentation in industrial laser processing Principal laser-based applications A composite system design in laser material processing applications Applications Neural Networks for Measurements and Instrumentation in Electrical Applications, Salvatore Baglio Instrumentation and measurement systems in electrical, dielectrical, and power applications. .. systems, for identification in instrumentation and measurement, for instrumentation and measurement dedicated to system and plant control, and for signal and image processing in instrumentation and measurement The underlying and unifying wire of the presentation is the interdisciplinary and comprehensive point of view of the metrological perspective Besides, it focus on the use, the benefits, and the... metrology and in the industrial applications, for mutual sharing of in-deepth interdisciplinary knowledge and to support further advancements both of the neural disciplines and the industrial application opportunities 1.4 The book topics From the NIMIA'2001 experience, this book tackles some of the most relevant areas in the use of neural networks for advanced instrumentation, measurement procedures and related. .. entertainment, and remote medical diagnosis and surgery Adaptivity and generalization ability of neural networks allow for introducing advanced features in these environments and to cope with non-linear aspects, dynamic variations of the operating conditions, and 6 V Piuri and S Ablameyko / Introduction to Neural Networks evolving environments The use of neural networks and their benefits are analyzed and evaluated.. .Neural Networks for Machine Condition Monitoring and Fault Diagnosis, Robert X Gao 1 2 3 4 5 9 9.1 9.2 9.3 10 10.1 10.2 10.3 10.4 10.5 11 11.1 11.2 11.3 12 12.1 12.2 12.3 12.4 12.5 Need for machine condition monitoring Condition monitoring of rolling bearings Neural networks in manufacturing Neural networks for bearing fault diagnosis Conclusions Neural Networks for Measurement and Instrumentation . 329 Neural Networks for Instrumentation, Measurement and Related Industrial Applications S. Ablameyko et al. (Eds.) IOS Press, 2003 Chapter 1 Introduction to Neural Networks for Instrumentation,. concerning the neural networks for sensors and measurement systems, for identification in instrumentation and measurement, for instrumentation and measurement dedicated to system and plant. methodologies for intelligent measurement systems 257 11.3 Industrial applications of soft sensors and neural measurement systems 263 12. Neural Networks for Measurement and Instrumentation

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  • Neural Networks for Instrumentation, Measurement and Related Industrial Applications

    • Neural Networks for Instrumentation, Measurement and Related Industrial Applications

      • Copyright

      • Preface

      • Acknowledgements

      • Contents

    • 1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications, Vincenzo Piuri and Sergey Ablameyko

      • 1.1. The scientific and application motivations

      • 1.2. The scientific and application objective

      • 1.3. The book organization

      • 1.4. The book topics

      • 1.5. The socio- economical implications

    • 2. The Fundamentals of Measurement Techniques, Alessandro Ferrero and Renzo Marchesi

      • 2.1. The measurement concept

      • 2.2. A big scientific and technical problem

      • 2.3. The uncertainty concept

      • 2.4. Uncertainty: definitions and methods for its determination

      • 2.5. How can the results of different measurements be compared?

      • 2.6. The role of the standard and the traceability concept

      • 2.7. Conclusions

    • 3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications, Stefano Ferrari and Vincenzo Piuri

      • 3.1. Introduction to intelligent measurement systems for industrial applications

      • 3.2. Design and implementation of neural- based systems for industrial applications

      • 3.3. Application of neural techniques for intelligent sensors and measurement systems

    • 4. Neural Networks in System Identification, Gabor Horvdth

      • 4.1. Introduction

      • 4.2. The main steps of modeling

      • 4.3. Black box model structures

      • 4.4. Neural networks

      • 4.5. Static neural network architectures

      • 4.6. Dynamic neural architectures

      • 4.7. Model parameter estimation, neural network training

      • 4.8. Model validation

      • 4.9. Why neural networks?

      • 4.10. Modeling of a complex industrial process using neural networks: special difficulties and solutions ( case study)

      • 4.11. Conclusions

    • 5. Neural Techniques in Control, Andrzej Pacut

      • 5.1. Neural control

      • 5.2. Neural approximations

      • 5.3. Gradient algebra

      • 5.4. Neural modeling of dynamical systems

      • 5.5. Stabilization

      • 5.6. Tracking

      • 5.7. Optimal control

      • 5.8. Reinforcement learning

      • 5.9. Concluding remarks

    • 6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing, Vladimir Golovko, Yury Savitsky and Nikolaj Maniakov

      • 6.1. Introduction

      • 6.2. Multilayer neural networks

      • 6. 3. Dynamical systems

      • 6. 4. How can we verify if the behavior is chaotic?

      • 6. 5. Embedding parameters

      • 6. 6. Lyapunov's exponents

      • 6. 7. A neural network approach to compute the Lyapunov's exponents

      • 6. 9. State space reconstruction

      • 6. 10. Conclusion

    • 7. Neural Networks for Image Analysis and Processing in Measurements, Instrumentation and Related Industrial Applications, George C. Giakos, Kiran Nataraj and Ninad Patnekar

      • 7. 1. Introduction

      • 7. 2. Digital imaging systems

      • 7. 3. Image system design parameters and modeling

      • 7. 4. Multisensor image classification

      • 7. 5. Pattern recognition and classification

      • 7.6. Image shape and texture analysis

      • 7.7. Image compression

      • 7.8. Nonlinear neural networks for image compression

      • 7.9. Linear neural networks for image compression

      • 7.10. Image segmentation

      • 7.11. Image restoration

      • 7.12. Applications

      • 7.13. Future research directions

    • 8. Neural Networks for Machine Condition Monitoring and Fault Diagnosis, Robert X. Gao

      • 8.1. Need for Machine Condition Monitoring

      • 8.2. Condition Monitoring of Rolling Bearings

      • 8.3. Neural Networks in Manufacturing

      • 8.4. Neural Networks for Bearing Fault Diagnosis

      • 8.5. Conclusions

    • 9. Neural Networks for Measurement and Instrumentation in Robotics, Mel Siegel

      • 9.1. Instrumentation and measurement systems for robotics: issues, problems, and techniques

      • 9.2. Neural network techniques for instrumentation, measurement systems, and robotic applications: theory, design, and practical issues

      • 9.3. Case studies: neural networks for instrumentation and measurement systems in robotic applications in research and industry

    • 10. Neural Networks for Measurement and Instrumentation in Laser Processing, Cesare Alippi and Anthony Blom

      • 10.1. Introduction

      • 10.2. Equipment and instrumentation in industrial laser processing

      • 10.3. Principal laser- based applications

      • 10.4. A composite system design in laser material processing applications

      • 10.5. Applications

    • 11. Neural Networks for Measurements and Instrumentation in Electrical Applications, Salvatore Baglio

      • 11.1. Instrumentation and measurement systems in electrical, dielectrical, and power applications

      • 11.2. Soft computing methodologies for intelligent measurement systems

      • 11.3. Industrial applications of soft sensors and neural measurement systems

    • 12. Neural Networks for Measurement and Instrumentation in Virtual Environments, Emil M. Petriu

      • 12.1. Introduction

      • 12.2. Modeling natural objects, processes, and behaviors for real- time virtual environment applications

      • 123. Hardware NN architectures for real- time modeling applications

      • 12.4. Case study: NN modeling of electromagnetic radiation for virtual prototyping environments

      • 12.5. Conclusions

    • 13. Neural Networks in the Medical Field, Marco Parvis and Alberto Vallan

      • 13.1. Introduction

      • 13.2. Role of neural networks in the medical field

      • 13.3. Prediction of the output uncertainty of a neural network

      • 13.4. Examples of applications of neural networks to the medical field

    • Index

    • Author Index

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