CODE 108766 ACADEMIC YEAR 2024/2025 CREDITS 6 cfu anno 1 INGEGNERIA GESTIONALE 8734 (LM-31) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/04 TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course presents the main modelling techniques for complex dynamical processes related to systems engineering and financial engineering. Moreover, the course addresses the identification methods necessary to evaluate the parameters present in the presented models. AIMS AND CONTENT LEARNING OUTCOMES Knowing the main modelling classes for dynamic processes with attention to those adopted in management engineering; defining a class of candidate models for a specific dynamic process; knowing the features of a parameter identification problem; designing the solution of an identification problem; analyzing the convergence properties of the adopted solution algorithm. AIMS AND LEARNING OUTCOMES The learning outcomes of the course refer to the capacity of: understand the dynamic features of a process; define a model suitable for representing the process and fulfil the objectives of the required analysis; knowing the features of an identification problem; knowing the most important classes of identification models; designing the solution of an identification problem. PREREQUISITES The course prerequisites refer to basic elements of systems theory, statistics and optimization. TEACHING METHODS The course offers classroom lessons covering theoretical concepts alongside the solution of numerical problems and the use of relevant software frameworks. SYLLABUS/CONTENT Definition of the main features of dynamic systems and to the main modelling classes. Introduction to the most important dynamic models adopted in management engineering and financial engineering. Identification techniques: definition of the parameter identification problem, model families (ARX, ARMAX, OE, ARXAR, BJ), MPE identification: convergence theorems, identification for ARX models (least squares identification), for ARMAX models and for ARXAR models, batch and iterative algorithms. Parametric and Bayesian estimation techniques. RECOMMENDED READING/BIBLIOGRAPHY L. Ljung, "System Identification: Theory for the user", Prentice Hall (2nd Edition), 1999. TEACHERS AND EXAM BOARD SIMONA SACONE Ricevimento: By taking an appointment with the teacher (simona.sacone@unige.it) LESSONS LESSONS START September 23, 2024 Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The exam is an oral presentation. ASSESSMENT METHODS During the exam the student has to present the main arguments of the course, to solve numerical exercises and to explain the theoretical notions necessary for their solution FURTHER INFORMATION Students with certified DSA, disabilities, or other special educational needs are advised to contact the teacher at the beginning of the course to agree on assessment methods considering individual learning needs, while respecting the teaching objectives.
