Industrial Control Systems: Mathematical and Statistical Models and Techniques (Hardcover, UK)

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