Course DSES 6961
Fuzzy Logic and Computational Intelligence
Tentative Course Outline
This course covers two important aspects of Artificial Intelligence:
the theory of Fuzzy Sets and Fuzzy Logics, and the area of Computational Intelligence.
We will conclude the course with cases studies and
advanced topics such as the representation of uncertainty in reasoning processes,
Case Based Reasoning, and Multi-Objective Evolutionary Algorithms.
- Fuzzy Set Theory and Fuzzy Logics.
The basic concepts of fuzzy set theory (membership, cardinality,
entropy) and set operations (union, intersection, complementation) are
described. Fuzzy sets are interpreted in the frame of possibility
theory. The difference with probability theory is pointed out. A
brief review of boolean logic and multivalued logic is given. Fuzzy
logic operations (and, or, not, implication), fuzzy relations and
compositions are then described. Triangular T-norms, conorms, and
generalized aggregation operators are also covered.
- Fuzzy Sets Applications.
We describe the use of F.S. in Decision Making, Failure Diagnosis, and
AI. First we define the Linguistic Approach, based on the concept of
linguistic variables, and the linguistic approximation. Then we
illustrate its applications to modeling, simulation and analysis of
complex, ill-defined systems. This approach, in combination with
fuzzy logics, has been extensively applied to decision analysis
(one/multi stage single/multi criteria decision making), failure
diagnosis (medical diagnosis, troubleshooting, maintenance) and
Artificial Intelligence (pattern recognition, cluster analysis, and
approximate reasoning). In our course we cover Decision Analysis and
Pattern Recognition/Cluster Analysis.
- Fuzzy Controllers (FC).
This is the core of the course. First, we will focus on Fuzzy
Controllers technology development. We will emphasize the use of
F.C. in Process Control (linguistic controllers, linguistic models).
We compare FLCs with with conventional controllers. We present them
as higher level language for the synthesis of Non-Linear Controllers.
We describe FC Development, Compilation, and Run-time, and its
supporting machinery (interpreter, compiler, and run-time engine.) We
discuss FL application to hierarchical control (supervisory mode) and
show examples of industrial applications. Finally we cover tuning
techniques and the symbiotic relationship between FL controllers and
Neural Networks.
- Computational Intelligence.
We will discuss the new field of Computational Intelligence (CI)
or Soft Computing (SC), a new discipline
that combines emerging problem-solving technologies such as Fuzzy
Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and
Genetic Algorithms (GAs). Within this broader context, we will
analyze and illustrate some of the most useful combinations of SC
components, such as the use of FL to control GAs and NNs parameters;
the application of GAs to evolve NNs (topologies or weights) or to
tune FL controllers; and the implementation of FL controllers as NNs
tuned by backpropagation-type algorithms.
- Case Studies.
We will analyze several cases studies taken from real-world applications, such as
fuzzy rule-based and case-based reasoning for insurance underwriting and property valuation;
evolutionary-tuned fuzzy controllers for regulation problems,
neuro-fuzzy models to predict remaining life in industrial processes,
multi-objective decision making using evolutionary search and fuzzy preferences for design optimization, etc.
- Advanced Topics.
- Representation of Uncertainty in Expert Systems and Soft Computing
We will discuss topics common to fuzzy sets and expert systems:
Bayesian Belief Networks, Theory
of belief and plausibility (Dempster-Shafer), Fuzzy necessity and
possibility.
- Evolutionary Multi-Objective Optimization (EMOO)
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