Soft Computing Techniques to Characterize Thermal Systems

Course guarantor:

Vladimír Zmrhal

Lecturer:

Arturo Pacheco-Vega

Tutor:

Arturo Pacheco-Vega, Vladimír Zmrhal

Supervisor:

Department of Environmental Engineering

Synopsis:

This course offers an applied overview of key soft computing methodologiesartificial neural networks, genetic algorithms, fuzzy logic, and cluster analysiswith a focus on their application in thermal and energy systems. Emphasis will be placed on coding and implementation of neural networks and genetic algorithms, as applied to data drawn from real-world thermal systems. Students will engage in hands-on projects involving numerical analysis, literature review, and technical presentations, potentially aligned with their own research activities.

Requirements:

Active participation is essential. Students are expected to engage in discussions, coding activities, and peer feedback throughout the course.

Coding and Tools: Students are expected to develop code using MATLAB, Fortran 77/90, or another language of their choosing, as long as they are proficient. Projects will involve algorithm implementation and data interpretation.

Syllabus of lectures:

The outline for the course is below; however, order and/or content may occur.

Artificial Neural Networks (ANNs) and Applications

oDefinitions and fundamentals

oTypes of ANNs: similarities and differences

oApplications to the characterization of thermal systems

Genetic Algorithms (GAs) and Applications

oDefinitions and fundamentals

oUse of GAs as a global optimization technique

oApplications to the characterization of thermal systems

Fuzzy Logic (FL) and Applications

oDefinitions and fundamentals

oTypes of inference systems

oApplications to the control of thermal systems

Cluster Analysis (CA) and Applications

oClassification algorithms

oMathematical fundamentals

oApplications to characterization of thermal systems

Syllabus of tutorials:

Course Format: The course is structured in a guided, discussion-based format with supplementary lectures. Key components include: Background lectures on soft computing and thermal system integration.

Team or individual projects involving data analysis, code development, and literature integration.

Student presentations on project results, emphasizing communication and critical evaluation.

Study Objective:

Understand the fundamentals of four soft computing techniques: ANN, GA, fuzzy logic, and clustering.

Apply soft computing toolsespecially neural networks and genetic algorithmsto analyze thermal system behavior.

Develop numerical coding skills tailored to soft computing applications in engineering.

Synthesize research findings through literature review and critical discussion.

Present technical findings and analyses in a professional format. Understand the fundamentals of four soft computing techniques: ANN, GA, fuzzy logic, and clustering.

Apply soft computing toolsespecially neural networks and genetic algorithmsto analyze thermal system behavior.

Develop numerical coding skills tailored to soft computing applications in engineering.

Synthesize research findings through literature review and critical discussion.

Present technical findings and analyses in a professional format.

Study materials:

Reference Materials: There is no required textbook, but selected references will be used throughout the course, including:

Haykin, S. (2002). Neural Networks: A Comprehensive Foundation (2nd Edition), Pearson Education.

Mitchell, M. (1996). An Introduction to Genetic Algorithms, MIT Press.

Additional research papers and technical documents tailored to specific topics.

Note:

Kapacita kurzu je omezena. Výuka probíhá pouze v angličtině.

Cell phones must be turned off or silenced during class. Also, texting is not allowed.

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed
Thu	roomT4:C2-233 Pacheco-Vega A. 09:00–10:30 (lecture parallel1) Dejvice roomT4:C2-233 Pacheco-Vega A. 10:45–11:30 (parallel nr.1) Dejvice
Fri

The course is a part of the following study plans: