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Industrial Control Systems: Mathematical and Statistical Models and Techniques / Edition 1

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More About This Textbook

Overview

Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techniques presents the mathematical foundation for building and implementing industrial control systems. The book contains mathematically rigorous models and techniques generally applicable to control systems with specific orientation toward industrial systems.

An amalgamation of theoretical developments, applied formulations, implementation processes, and statistical control, the book covers:

  • Industrial innovations and systems analysis
  • Systems fundamentals
  • Technical systems
  • Production systems
  • Systems filtering theory
  • Systems control
  • Linear and nonlinear systems
  • Switching in systems
  • Systems communication
  • Transfer systems
  • Statistical experimental design models (factorial design and fractional factorial design)
  • Response surface models (central composite design and Box–Behnken design)

Examining system fundamentals and advanced topics, the book includes examples that demonstrate how to use the statistical designs to develop feedback controllers and minimum variance controller designs for industrial applications. Clearly detailing concepts and step-by-step procedures, it matches mathematics with practical applications, giving you the tools to achieve system control goals.

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Product Details

  • ISBN-13: 9781420075588
  • Publisher: CRC Press
  • Publication date: 11/14/2011
  • Series: Industrial Innovation Series , #22
  • Edition number: 1
  • Pages: 380
  • Product dimensions: 6.30 (w) x 9.20 (h) x 1.10 (d)

Table of Contents

Mathematical Modeling for Product Design
Introduction
Literature Review
Memetic Algorithm and Its Application to Collaborative Design
Conclusion
References

Dynamic Fuzzy Systems Modeling
Introduction: Decision Support Systems, Uncertainties
Decision Support Systems
Uncertainty
Fuzziness
Fuzzy Set Specifications
Stochastic–Fuzzy Models
Applications
Conclusions
References

Stochastic Systems Modeling
Introduction to Model Types
Systems Filtering and Estimation
Correlation Techniques
Model Control—Model Reduction, Model Analysis
References

Systems Optimization Techniques
Optimality Conditions
Basic Structure of Local Methods
Stochastic Central Problems
Intelligent Heuristic Models
Heuristics
References

Statistical Control Techniques
Statistical Process Control
Control Charts
Process Capability Analysis
Time Series Analysis and Process Estimation
Time Series Analysis Example
Exponentially Weighted Moving Average
Cumulative Sum Chart
Statistical Process Control
Automatic Process Control
Criticisms of SPC And APC
Overcompensation, Disturbance Removal, and Information Concealing
Integration Of SPC And APC
Systems Approach To Process Adjustment
ARIMA Modeling Of Process Data
Model Identification And Estimation
Minimum Variance Control
Process Dynamics With Disturbance
References

Design of Experiment Techniques
Factorial Designs
Factorial Design for 3 Factors
Saturated Designs
Central Composite Designs
Response Surface Optimization
References

Risk Analysis and Estimation Techniques
Bayesian Estimation Procedure
Computational Procedure
Parameter Estimation for Hyperbolic Decline Curve
Robustness of Decline Curves
Mathematical Analysis
Statistical Analysis
Parameter Estimation
Optimization Technique
Iterative Procedure
Residual Analysis Test
Simplified Solution to the Vector Equation
Integrating Neural Networks And Statistics For Process Control4
Fundamentals of Neural Network
The Input Function
Transfer Functions
Statistics and Neural Networks Predictions
Statistical Error Analysis
Integration of Statistics And Neural Networks
References

Mathematical Modeling and Control of Multi- Constrained Projects
Introduction
Literature Review
Methodology
Representation of Resource Interdependencies and Multifunctionality
Modeling of Resource Characteristics
Resource Mapper
Activity Scheduler
Model Implementation And Graphical Illustrations
Notations
References

Online Support Vector Regression with Varying Parameters for Time-Dependent Data
Introduction
Modified Gompertz Weight Function for Varying SVR Parameters
Accurate Online SVR with Varying Parameters
Experimental Results
Conclusion
References
Appendix: Mathematical and Engineering References
Index

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