Cover -- Title page -- Preface -- Contents -- 1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 6.8 Prediction of chaotic processes by using neural networks -- 6.9 State space reconstruction -- 6.10 Conclusion -- 7. Neural Networks for Image Analysis and Processing in Measurements, Instrumentation and Related Industrial Applications -- 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 -- 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 -- 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 -- 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 -- 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 -- 12.1 Introduction -- 12.2 Modeling natural objects, processes, and behaviors for real-time virtual environment applications -- 12.3 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 -- 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 -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- U -- V -- W -- Z -- Author Index.