Advanced Systems Engineering |
YOSHITANI, Naoharu |
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【Master's program･2nd semester】
16-3-1015-2016 |
| １． |
Objectives |
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The main objective is for students to learn and understand the principles and important techniques of systems engineering. Lectures are given in such a way that even students without basic knowledge of systems engineering can understand the lectures.
‘System’ means a whole set of interacting components. Ecological systems in natural world consist of interacting living creatures, and intranet computer systems consist of computers communicating with each other. ‘Systems engineering’ deals with the investigation, analysis, planning and operation of various systems. It has become more important as economic developments and advancing technologies have increasingly greater impact on the natural environment and make artificial systems more complicated and dominant.
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Outline |
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This course consists of lectures in the classroom with exercises consisting of practical problems, and laboratory works in the computer laboratory (CL). Important contents to be learned in this course are:
1. Introduction, modeling principles
2. Outline of mathematical and graphical models
4. Techniques of mathematical modeling
5. Simulations of deterministic systems
6. Probability distribution, simulation of a queuing system
7. Optimization 1: mathematical programming, linear/nonlinear programming
8. Optimization 2: genetic algorithm
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Requirements (Assignments) |
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Students are expected to have a basic understanding in mathematics at the undergraduate level, differentials, integrals and differential equations in particular. Students who are in some degree lacking in background in any of these fields are required to study those required fields.
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Schedule |
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1. Introduction to systems engineering
2. Systems description—mathematical models 1: differential equation, transfer function
3. Systems description—mathematical models 2: least squares method, multiple regression
4. Laboratory work 1: multiple regression
5. Systems description—graphical models: state transition, adjacency matrix
6. Differential equation and simulation: Euler's method, Runge-Kutta method
7. Laboratory work 2: simulation of differential equations
8. Probability distribution 1: uniform/normal distribution, random number generation
9. Probability distribution 2: Poison/exponential distribution
10. Stochastic systems and simulation, queuing system
11. Laboratory work 3: queuing system
12. System optimization 1: linear programming (LP)
12. System optimization 2: nonlinear programming (NLP)
14. Laboratory work 4: linear and nonlinear programming
15. Genetic algorithm, reviews and exercises
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Grading Policy |
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Grading policy is based on the results of the answer sheets of exercises and reports on laboratory works.
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Textbook and Reference |
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Textbook: H. Tamura, et al., "Systems engineering", Ohm publishing Co. (in Japanese)
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Note |
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