KA224001 Stochastic Processes

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Informatics and Data Science

Lecture Code KA224001 Subject Classification Specialized Education

Subject Name 確率過程論

Subject Name
（Katakana）カクリツカテイロン

Subject Name in
English Stochastic Processes

Instructor KAWANO YU

Instructor
(Katakana) カワノ　ユウ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Weds7-10：ENG 218

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

lecture, exercises

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords system modeling, state-space models, controllability and observability, stability, feedback control, observers, optimal control, probability.

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline This course covers fundamentals of automation technologies, such as drone autopilots and autonomous driving systems. In particular, the objective is to develop an understanding of the basics of system control based on mathematical modeling. The course specifically addresses the following topics.
(1) State equations: mathematical modeling of dynamical systems using differential equations
(2) Stability: one of the fundamental properties to be achieved in control systems
(3) Controllability and observability: fundamental properties related to the placement of actuators and sensors
(4) State feedback control, observers, and optimal regulators: representative control methods
(5) Stochastic disturbances: analysis to account for the gap between mathematical models and real-world systems

Class Schedule lesson 1: Guidance
lesson 2: State-space representation
lesson 3: Solutions to state equations
lesson 4: Stability
lesson 5: Controllability
lesson 6: Controllable canonical form and controllability decomposition
lesson 7: Observability
lesson 8: Observable canonical form and observability decomposition
lesson 9: State feedback control
lesson 10: Observers
lesson 11: Optimal regulators
lesson 12: Servo control
lesson 13: State-space representation with stochastic disturbances
lesson 14: Kalman filter
lesson 15: Overall Review

report, final examination

Text/Reference
Books,etc. Handouts will be distributed at each lecture.

PC or AV used in
Class,etc. Handouts, moodle

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Review the material so that you can understand the derivations of the theory and the computational methods.

Requirements A basic knowledge of calculus and linear algebra is assumed.

Grading Method Reports: 40%, Final exam: 60% (The final exam may be replaced with a take-home assignment in some cases.)

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA224001	Subject Classification	Specialized Education
Subject Name	確率過程論
Subject Name （Katakana）	カクリツカテイロン
Subject Name in English	Stochastic Processes
Instructor	KAWANO YU
Instructor (Katakana)	カワノ　ユウ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Weds7-10：ENG 218
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
lecture, exercises
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	system modeling, state-space models, controllability and observability, stability, feedback control, observers, optimal control, probability.
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Data Science Program （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	This course covers fundamentals of automation technologies, such as drone autopilots and autonomous driving systems. In particular, the objective is to develop an understanding of the basics of system control based on mathematical modeling. The course specifically addresses the following topics. (1) State equations: mathematical modeling of dynamical systems using differential equations (2) Stability: one of the fundamental properties to be achieved in control systems (3) Controllability and observability: fundamental properties related to the placement of actuators and sensors (4) State feedback control, observers, and optimal regulators: representative control methods (5) Stochastic disturbances: analysis to account for the gap between mathematical models and real-world systems
Class Schedule	lesson 1: Guidance lesson 2: State-space representation lesson 3: Solutions to state equations lesson 4: Stability lesson 5: Controllability lesson 6: Controllable canonical form and controllability decomposition lesson 7: Observability lesson 8: Observable canonical form and observability decomposition lesson 9: State feedback control lesson 10: Observers lesson 11: Optimal regulators lesson 12: Servo control lesson 13: State-space representation with stochastic disturbances lesson 14: Kalman filter lesson 15: Overall Review report, final examination
Text/Reference Books,etc.	Handouts will be distributed at each lecture.
PC or AV used in Class,etc.	Handouts, moodle
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Review the material so that you can understand the derivations of the theory and the computational methods.
Requirements	A basic knowledge of calculus and linear algebra is assumed.
Grading Method	Reports: 40%, Final exam: 60% (The final exam may be replaced with a take-home assignment in some cases.)
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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