Neural Networks and Fuzzy Logic in Control
CMPE Degree: This course is for the CMPE degree.
EE Degree: This course is for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Systems and Controls
Prerequisites: ECE 3085/3550
Catalog DescriptionPrinciples of neural networks and fuzzy systems; the MATLAB Neural Network
and Fuzzy Logic Toolboxes; examples from system identification,
classification and control; laboratory experience.
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( ) An ability to communicate effectively with a range of audiences
4. ( ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
* Introduction/Motivation (1 week)
- What is Intelligent Control?
- Attributes of Intelligent Behavior
- Dealing with Uncertainty
* Data Management (1 week)
- Statisical and other
- Methods for Data Processing
* Neural Networks (4 weeks)
- Introduction to neural networks; the biological neuron; thresholding
- Classical neural network models
- Learning Rules and the Backpropagation Algorithms
- The MATLAB Neural Network Toolbox
- Neural Networks in Control Applications
* Genetic Algorithms (1 week)
- Applications to Optimization Problems
* Fuzzy Sets and Fuzzy Logic (1 week)
- Fuzzy arithmetic
- Fuzzy set operations
- Fuzzy Logic, inferencing and approximate reasoning
* Fuzzy Logic Control (2 weeks)
- Heuristic methods
- A systematic fuzzy logic control design methodology
* Fuzzy Tools (1 week)
- Software and firmware tools
- Laboratory demonstrations
* The Neuro-Fuzzy Connection (2 weeks)
- Identification and Control
* Class Projects (1 week)