This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable reference guide for all engineers and researchers in the field of computer science who are interested in intelligent systems, rule-based systems and modeling uncertainty. 本书重点论述二型模糊逻辑的一个特定域，与过程建模和控制应用相关。通过阅读本书将加深读者对二型模糊逻辑在如下方面的理解：利用简单的方法训练二型 Takagi-Sugeno 模型；利用二型模糊逻辑原理减少建模上的局部线性N步超前预测的不确定性的影响；以及利用二型模糊集，根据广义预测控制原则来开发基于模型的控制算法。 在整本书中，理论总是与实际应用相辅相承，读者可以利用本书提供算法源代码实践自己的应用，将学习过程向前推进一步。因此，本书为对智能系统、规则系统和模型的不确定性感兴趣的计算科学领域所有研究者和工程师提供了非常有价值的参考。

前辅文
1 Introduction
  1.1 Book Outline
  References
2 Fuzzy Logic Systems
  2.1 Introduction
  2.2 Type-1 Fuzzy Sets
  2.3 Type-1 Fuzzy Logic Systems
   2.3.1 Fuzzifier
   2.3.2 Rule-Base
   2.3.3 Inference Engine
   2.3.4 Output Processor
   2.3.5 Considerations About Type-1 Fuzzy Logic Systems
  2.4 Type-2 Fuzzy Sets
  2.5 Type-2 Fuzzy Logic Systems
   2.5.1 Fuzzifier
   2.5.2 Rule-Base
   2.5.3 Inference Engine
   2.5.4 Type-Reduction
   2.5.5 Defuzzifier
  2.6 Comparative Analysis
  2.7 Conclusions
  References
3 Takagi-Sugeno Fuzzy Logic Systems
  3.1 Introduction
  3.2 Type-1 Takagi-Sugeno Fuzzy Logic Systems
  3.3 Type-2 Takagi-Sugeno Fuzzy Logic Systems
   3.3.1 A2-C1 Structure
   3.3.2 A2-C0 Structure
   3.3.3 A1-C1 Structure
  3.4 ANFIS Based on Type-2 TS Fuzzy Logic Systems
  3.5 Training Algorithms for TS Fuzzy Systems
   3.5.1 Model Initialization
   3.5.2 Training of the Antecedent Part of the Rule Base
   3.5.3 Training of the Consequent Part of the Rule Base
  3.6 Conclusions
  References
4 System Modeling Using Type-2 Takagi-Sugeno Fuzzy Systems
  4.1 Introduction
  4.2 Locally Linear Models Based on Type-2 TS Fuzzy Logic Systems
   4.2.1 Development of the Interpolated Interval Type-2 Fuzzy Model
   4.2.2 Development of the n-step Ahead Predictor
  4.3 Application Scenarios
   4.3.1 Fermentation Reactor Modeling
   4.3.2 Coupled Tanks Modeling
  4.4 Conclusions
  References
5 Model Predictive Control Using Type-2 Takagi-Sugeno Fuzzy Systems
  5.1 Introduction
  5.2 Generalized Predictive Control
  5.3 Derivation of a n-step Ahead Predictor
  5.4 Extension of Generalized Predictive Control to Non-linear Models
   5.4.1 Generalized Predictive Control Using Type-2 TS Fuzzy Models
  5.5 Application Scenarios
   5.5.1 Fermentation Reactor’s Temperature Control
   5.5.2 Coupled Tanks Liquid Level Control
  5.6 Conclusions
  References
6 Processor-In-the-Loop Simulation
  6.1 Introduction
  6.2 PIL Architecture
   6.2.1 Development Board
   6.2.2 Embedded System’s Software Architecture
  6.3 System Evaluation
  6.4 Conclusions
  References
7 Conclusions
Appendix