engineering management

Orale

Students will learn how to efficiently write code for applications related to business intelligence, data analysis and data science. Students will learn the fundamentals of Python, the main programming language used in data science applications such as data analysis and artificial intelligence. Students will learn the ability to analyze a problem, the ability to inspect data and represent data with a programming language. Students will also acquire knowledge relating to the functioning of the Python language, also tied to computational aspects in order to solve problems efficiently.
Students will acquire knowledge on datamining algorithms and their practical applications. Students will learn how to set up a datamining application and to face classical tasks such as searching by similarity, basket analysis, data clustering, and item recommendation.
Students will acquire knowledge on Agentic AI to automate tasks and interact with data via natural language.

Programming for data science
1.1 Python programming
Introduction to the Python programming language: basic syntax; data structures; object-oriented programming; libraries and modules.
1.2 Libraries for data analysis
The scipy ecosystem; numpy arrays; pandas data structures; plotting with Matplotlib; linear algebra using numpy; examples of scientific computing with scipy.
Datamining
2.1 Introduction to dataming
Statistical Modeling. Machine Learning. Data collection and analysis. Big data.
2.2 Similar Items
Similar items. Set similarity. Hash functions. Hashing shingles. Minhashing and Jaccard Similarity. Minhash signatures. Locality Sensitive Hashing (LSH): bands, candidate pairs. Distance measures. Locality Sensitive functions. AND and OR constructions. LSH for cosine distance.
2.3 Frequent Itemsets and Basket Analysis
The market-basket model. Frequent itemsets. Support. Association rules. Confidence and interest. Counting itemsets. The monotonicity property. The A-priori algorithm.
2.3 Clustering
Hierarchical clustering. K-means algorithms. Density-based algorithms.
3.3 Recommendation Systems
Utility matrix. The long-tail phenomenon. Content-based recommendation. Text representation: vector space; Latent Semantic Indexing. User profiles and Preference prediction. Collaborative filtering: user-user and item-item similarity. Hierarchical clustering. Latent factor recommendation.
Agentic AI
3.1 Core Concepts of AI Agents
From LLMs to Agents: understanding the difference between static text generation and goal-directed autonomous workflows. Utilizing the Large Language Model as a central reasoning engine. Reasoning Frameworks. Introduction to the ReAct (Reason + Act) prompting paradigm. Agent Components. Memory (short-term vs. long-term context) and Tool Calling (equipping agents with calculators, APIs, or database access).
3.2 Agentic Applications in Business Intelligence
Text-to-SQL. Enabling agents to translate natural language questions into executable database queries. Automated Data Analysis. Designing agents that autonomously write and execute Python code to analyze datasets and surface insights. Retrieval-Augmented Generation (RAG). Grounding agent responses in specific enterprise documents to reduce hallucinations.

Basic knowledge of programming (in any programming language) is preferred but not strictly required.

Lectures and practical sessions, in-class questions and homework exercises. The skills to be achieved are a theoretical understanding of the Python programming language, the ability to evaluate which constructs to use to implement software solutions aimed at data analysis. Problem-solving ability of complex problems aimed at acquiring, analyzing and processing data.

Oral exam consisting of questions on the course contents and in the presentation of a project (Practical implementation of a datamining/data analysis task using python on a dataset selected by the student).

Lecture slides
J. Leskovec, A. Rajaraman, J.D. Ullman, “Mining of Massive Datasets”

The course presents an introduction to complex systems. Moreover, the theory of nonlinear dynamic systems, including bifurcation theory, Lyapunov stability methods, and qualitative analysis, is presented. Students will learn to assess the local and global stability of equilibria through both linearization techniques and direct Lyapunov functions. Algorithms for the simulation of complex dynamics, such as limit cycle oscillations and chaotic behavior, are presented. Several examples of complex systems in discrete and continuous time are tackled, with application to population dynamics, economic, ecological, and biological systems.

Modeling and analysis of complex systems. Introduction of fundamental mathematical concepts for understanding and analyzing autonomous linear and nonlinear ordinary differential equations. The qualitative analysis of differential equations is emphasized. Criteria and theorems for the asymptotic stability of equilibria using linearization and Hartman-Grobman theorem are presented. Moreover, linear and nonlinear oscillations (limit cycles) are discussed related to bifurcations. Main bifurcations discussed are saddle-node, transcritical, pitchfork and Hopf bifurcations. Additionally, period-doubling in continuous time systems, and flip in discrete-time systems are introduced, in relation to bifurcation cascades and routes to chaos. Chaotic attractors and fractals. Synchronization. Simulation of nonlinear systems. Software tools for the analysis of complex systems: MATLAB. Analysis and simulation of complex systems in multidisciplinary fields: physical, biological, ecological and economic systems. Direct and indirect methods for stability analysis: Lyapunov stability theory. Construction of scalar Lyapunov functions for continuous-time systems. LaSalle’s Invariance Principle for the asymptotic stability of non-strict Lyapunov functions and the identification of attraction basins. Extended Program. 1. Continuous time systems - Linear and nonlinear differential equations - Vector fields, phase space and differential equations - Stability of steady states - Linearization of nonlinear systems - Lyapunov stability theory: Direct (second) method of Lyapunov: positive definite functions and derivative along trajectories. Construction of candidate Lyapunov functions for physical and non-physical systems. LaSalle’s Invariance Principle and basin of attraction estimation - Oscillating solutions of nonlinear systems - Simulations and examples (labs) 2. Discrete time systems - Linear and nonlinear maps - Stability of the fixed points of maps - The logistic map - Iterations of maps (labs) 3. Bifurcations - Saddle-Node bifurcation - Transcritical bifurcation - Pitchfork bifurcation - Hopf Bifurcation - Flip bifurcation - Period doubling bifurcation - Simulations and examples (labs)

Basics on Dynamic Systems, Physics and Mathematics

The course includes theoretical lectures and practical laboratories. The course web site presents detailed information on program, teaching material and organization of the exams.

The student's proficiency is evaluated in two steps. The first step is a written test, which includes the solution of exercises and the answer to theoretical questions. The test requires the use of Matlab. The second step is a Master Seminar of 15 minutes on a subject related to the topics presented during the course. The subject is free and must be chosen by the student.

Steven Strogatz, Nonlinear Dynamics and Chaos, Westview, 2000

The course provides the students with the fundamentals of data analysis, including hypothesis testing, linear regression and classification. Throughout the course, special emphasis is put on the correct interpretation of the results of the analysis process. At the end of the course, students will be able to properly formulate simple data analysis problems and to solve them by using Python.

Introduction to data analysis. Review of Probability, Law of large numbers, Central limit theorem. Describing a data set: Sample mean and variance, Percentiles, Boxplot, Histogram, Sample correlation coefficient. Confidence intervals: Mean, Variance, Proportion. Estimating a PDF: Histogram, Maximum Likelihood Estimation.
Introduction to hypothesis testing: Null and alternative hypotheses, Level of significance, p-value. Courtroom trial analogy, Types of error, One-sided and two-sided tests. t-Tests on the mean: 1-sample, 2-sample, paired t-Test. Tests on variance and proportion: Chi-2 test (variance), F-test, z-Test. Tests on uncorrelation and independence (categorical variables): t-Test, Chi-2 test. Further topics in hypothesis testing: Other common tests, Normality assumption, Connection with confidence intervals.
Introduction to model estimation: Prediction and Inference, Parametric vs Non-Parametric approach, Model Accuracy. Simple linear regression: Coefficient estimates, Accuracy of coefficient estimates, Model accuracy. Multiple linear regression (MLR): Coefficient estimates, Accuracy of coefficient estimates, Model accuracy, Selecting important variables. Prediction and prediction intervals, qualitative input variables in MLR. Further topics in MLR: Residual plot, Outliers, Non-constant variance, Collinearity, Log transform.
Introduction to classification, Logistic regression. Linear discriminant analysis (LDA). Quadratic discriminant analysis (QDA). Naive Bayes (NB) and K-nearest neighbors (KNN).
Using Python as software tool for data analysis

Basics of probability and statistics.

Lectures and lab exercises.

The exam consists in a project work and an oral examination, which includes a discussion on the developed project. The project work allows the student to apply the methodologies presented in the course to real world problems. The oral examination aims at verifying that the student has acquired the basic notions of data analysis.

[PSES] Introduction to Probability and Statistics for Engineers and Scientists, 5th ed., S. Ross, Academic Press, 2014
[ISL] An Introduction to Statistical Learning: with Applications in Python, 1st ed., G. James, D. Witten, T. Hastie, R. Tibshirani, J. Taylor, Springer, 2023.
[PDA] Python for Data Analysis: Data Wrangling With Pandas, Numpy, and Jupyter, McKinney, O'Reilly, 2022
Additional teaching material will be made available by the instructor during the course

The course provides students with theoretical and practical tools for modeling and analysis of DES, focusing mainly on applications in automated manufacturing and computer science. Through tutorials and a project, students apply these tools to real-world scenarios.
Contribution to Degree Program Objectives:

• In-depth knowledge of DES models and theory
• Ability to build and apply DES models in real-world problems
• Ability to evaluate validity, effectiveness and costs of innovative solutions in real-world DES
• Rigor in communicating ideas and solutions in real-world DES applications
• Critical and autonomous problem-solving skills in the field of DES
  Detailed expected learning outcomes:
• Model DES both in deterministic and stochastic settings
• Provide mathematical formulations of analysis problems for DES both in deterministic and stochastic settings
• Simulate DES models and apply statistical techniques to assess performance
• Use accurate and rigorous terminology regarding DES
• Synthesize theoretical and methodological knowledge on DES into a unified view

Discrete event systems (DES) are systems whose dynamic behavior is driven by asynchronous occurrences of discrete events. Examples can be found in a variety of fields, such as control, computer science, automated manufacturing, and communication and transportation networks. This course introduces several modelling and analysis tools for DES. Emphasis is given to dealing with uncertainty ubiquitous in real-world applications. Students also learn how to simulate DES and evaluate performance.
The following main topics are addressed in the course:

• Definition of event-driven dynamical systems
• Untimed models of DES
• Timed models of DES
• Stochastic timed models of DES
• Continuous-time homogeneous Markov chains
• Stochastic equivalence of DES models
• Queueing theory
• Discrete event simulation and performance evaluation

Students are expected to have basic understanding of linear dynamical systems (e.g., state-space representations), and basic knowledge of linear algebra (e.g., matrices, vectors, eigenvectors), probability theory and statistics, including concepts such as total probability rule, independence, conditional probability, random variables, expected value and variance, law of large numbers. Programming experience is recommended but not required.

In-class time is structured into:

• Lectures (covering theoretical and methodological aspects of DES)
• Exercise sessions (showing solution strategies for selected written exercises)
• Lab tutorial sessions (addressing implementation and simulation of discrete-event models)
  Out-of-class work consists of:
• Individual study (theory and exercises)
• Group project on modeling, simulation and analysis of a real-world discrete-event system
  A visit to a manufacturing plant will be organized, if possible, to watch practical applications of DES in automated manufacturing.

Assessment is structured as follows (between parentheses the weight assigned to each test):

• A written test (30%) evaluates modeling skills and the ability to provide mathematical formulations of analysis problems
• A project work (30%) evaluates modeling skills and the ability to carry out as-is and what-if analysis via simulation
• An oral test (40%) evaluates the student’s ability to synthesize and discuss the key theoretical and methodological concepts, and the experience made with the project

C.G. Cassandras, S. Lafortune, “Introduction to discrete event systems”. 2nd ed., Springer, 2008.
Link Leganto: https://sbart-unisi.alma.exlibrisgroup.com/leganto/public/39SBART_SBS_MAIN/lists/21129780600003297?auth=LOCAL

L'economia manageriale è l'economia applicata ai processi decisionali. E' una parte dell'economia dedicata a colmare i divari tra la teoria e la pratica manageriale. L'obiettivo del corso è di fornire agli studenti i gli strumenti della microeconomia e dell'economia industriale necessari per prendere corrette decisioni manageriali. Lo studente acquisirà conoscenze e metodologie per affrontare problematiche relative a : - problemi economici reali; - dimostrazioni economiche; - come che le teorie economiche risolvono le diverse situazioni aziendali; - capire e sapere discutere le differenze dei diversi mercati. _________________________________________________________________ Managerial Economics is economics applied in decision-making. It is a special branch of economics bridging the gap between abstract theory and managerial practice. The fundamental goal of the course is to provide to students the tools of microeconomics and industrial organization that they need to make sound managerial decisions. The student will acquire knowledge and methodologies to address in an original way the main issues related to: - real-world economic problem; - economic demonstration problems; - how economic theories relate to a host of different business situations; - understand and discuss market differences.

Richiami di microeconomia: Fondamenti di economia manageriale, domanda e offerta, analisi quantitativa della domanda, la funzione di produzione e di costo;
Le decisioni nei mercati caratterizzata da competizione perfetta;
Il monopolio;
La concorrenza monopolistica e la differenziazione di prodotto;
Modelli di oligopolio;
La struttura dell’industria
Strategie di prezzo avanzate e la discriminazione di prezzo;
Argomenti avanzati di strategie di impresa ed economia industriale;
I mercati a rete e le piattaforme;
Casi di studio.
______________________________________
Review of microeconomics: The fundamentals of managerial economics, demand and supply, Quantitative demand analysis, The production process and costs;
Managing in competitive markets;
Monopoly;
Monopolistic competition and product differentiation;
Oligopoly models;
The nature of industry;
Pricing strategies and price discrimination;
Advanced topics in Business strategy;
Networked markets and Platforms
Case studies

Aspetti base di economia e organizzazione aziendale _______________________________________ Principles of Economics and Organization

Lezioni, esercitazioni, casi di studio ____________________ Lectures, exercises, case studies

L'esame consiste di una parte scritta e una orale. Lo scritto consiste nella soluzione di alcuni esercizi quantitativi sui modelli studiati, l'orale riguarda la conoscenza dei modelli e dei problemi trattati. Parte scritta ed orale devono essere superati nella stessa sessione d’esame (non è possibile svolgere la prova scritta in una sessione e l’orale in un’altra). ________________________________________________________ The examination consists in a written test (quantitative exercises on studied models) and an oral test (knowledge of problems and solution models). It is need to complete written and oral test in the same exam (it is not possible to do the written test in one session and the oral test in another session)

Baye M.R., Prince J.T. “Managerial economics and business strategy”. Mcgraw-hill Cabral, L. M. "Introduction to industrial organization" 2nd Edition. MIT press. Brander J, Perloff J, “Managerial Economics and Strategy”, Pearson. Cabral, L.M. "Economia Industriale - nuova edizione". Carocci editore.

PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. LOGISTICS The objective of the course is to introduce to the problems, the mathematical models, as well as to their solution approaches related to internal and external logistics of production and distribution systems. PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING The objective of the course is to introduce the student to the main quantitative decision models for supply chain planning and management, as well as to their solution approaches.
PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. LOGISTICS Il corso ha lo scopo di introdurre lo studente ai problemi, ai modelli matematici e ai principali metodi di soluzione relativi alla logistica interna ed esterna dei sistemi di produzione e distribuzione. PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING Conoscere i principali modelli di pianificazione della produzione e in un impianto produttivo, gli approcci metodologici per affrontarli e i principali algoritmi per la loro soluzione.

PRODUCTION AND SUPPLY CHAIN MANAGEMENT -mod. LOGISTICS - Structures of the logistic networks. Coordination problems in supply chains. Management problems in material flows: Minimum cost network flows. Structure of the optimal solutions. The network simplex. External logistics: Plant location, Vehicle routing. SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING Supply chain management. Flow management problems: lot sizing, contiuous and discrete models. Materials Requirement Planning, just-in-time. Push vs. pull production systems.Complex scheduling problems. Flow shop, Job shop, assembly line balancing. Exact and heuristic methods.
PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. LOGISTICS I problemi di coordinamento delle filiere produttive. La funzione logistica. Struttura delle reti logistiche. Problemi di gestione dei flussi materiali. Leve gestionali. Problemi di costo su reti di flusso. Struttura delle soluzioni. Il simplesso su reti. Problemi strategici: plant location. Logistica interna: approcci e modelli per la gestione della produzione. Logistica esterna: Plant location, Vehicle routing. SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING Gestione della produzione industriale. Gestione dei flussi materiali: controllo delle scorte, dimensionamento dei lotti. Modelli continui e discreti. MRP, just-in-time. Sistemi push e pull. Problemi di scheduling complessi. Flow shop, Job shop, bilanciamento linee di assiematura. Metodi esatti e euristici.

The basic notions of linear and discrete optimization.
Le nozioni di base di ottimizzazione lineare e discreta.

Lectures. Some lab sessions will also be organized.
Lezioni frontali. Alcune esercitazioni si svolgeranno in laboratorio.

mod. LOGISTICS The examination has a written and an oral part. Written and an oral parts have the same weight and equally contribute to the final score. The written part consists of modeling and numerical exercises on logistics problems introduced during the course. The oral part is devoted to verify the knowledge of the theoretical foundations of the models and the solution methods of the addressed logistics problems. At the end of the course, before the official date of the first exam session, a preliminary written test can be performed with numerical exercises and theoretical questions. A good performance in the test allows to perform an examination without the written part and with a reduced oral exam, that consists in answering to one or two questions.
mod. PRODUCTION PLANNING The examination consists in a written test (quantitative exercises on studied models) and an oral test (knowledge of problems and solution models). During the course, two intermediate written tests will be organized. If a student passes both of them, the written test can be waived.
---------------------------
A single score is assigned to the course given by the average of the scores attained in the two parts: mod. LOGISTICS and mod. PRODUCTION PLANNING
mod. LOGISTICS La prova d'esame prevede una prova scritta ed una orale. Ai fini del voto finale, le prove scritte ed orali hanno lo stesso peso. Lo scritto consiste in esercizi relativi alla modellazione e alla soluzione di alcuni problemi introdotti nel corso. La prova orale mira a verificare la conoscenza delle basi teoriche per la modellazione e la soluzione dei suddetti problemi. Al termine del corso, prima dell'appello ufficiale, si svolge una prova scritta che comprende esercizi e domande di teoria. A seconda del voto conseguito nella prova, essa dà diritto a superare l'esame o a sostenere una prova orale in forma ridotta, composta di una o due domande.
mod. PRODUCTION PLANNING L'esame consiste di una parte scritta e una orale. Lo scritto consiste nella soluzione di alcuni esercizi quantitativi sui modelli studiati, l'orale riguarda la conoscenza dei modelli e dei problemi trattati. Durante il corso sono organizzate due prove scritte che, se superate, possono sostituire lo scritto di esame. ---------------------------
Un singolo voto è assegnato al corso dato dalla media dei voti ottenuti nei due moduli: mod. LOGISTICS and mod. PRODUCTION PLANNING

The list of the texts is available at the following link: https://sbart-unisi.alma.exlibrisgroup.com/leganto/public/39SBART_SBS_MAIN/lists/18756592780003297 PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING Instructor's notes and published research papers.
La lista dei testi è disponibile al link: https://sbart-unisi.alma.exlibrisgroup.com/leganto/public/39SBART_SBS_MAIN/lists/18756592780003297 PRODUCTION AND SUPPLY CHAIN MANAGEMENT - mod. PRODUCTION PLANNING Dispense ed articoli scientifici.

Il corso mira a fornire agli studenti una conoscenza complessiva dei principi, dell'evoluzione e delle principali metodologie di Project Management, sviluppando la capacità di applicare tali conoscenze a casi di progetto.
Particolare attenzione è dedicata allo sviluppo di comptentze come problem solving, decision making e team working, sempre attraverso l'analisi di project cases.
Il corso si concentra in particolare sulle seguenti aree:
Gestione del ciclo di vita del progetto, dalla definizione degli obiettivi alla pianificazione, esecuzione, monitoraggio e chiusura;
Ruolo e responsabilità del Project Manager.
Pianificazione e gestione delle risorse di progetto.
Applicazione pratica degli strumenti e delle metodologie di Project Management attraverso esercitazioni, casi e simulazioni.
Un ulteriore obiettivo fondamentale del corso è sviluppare la capacità degli studenti di lavorare efficacemente in team in un contesto di progetto.
Gli studenti saranno organizzati in team e lavoreranno durante il corso su project case e simulazioni, affrontando situazioni, problemi e cambiamenti che richiederanno analisi, confronto e decisioni condivise.
Durante le sessioni di lavoro, i team saranno chiamati a:
analizzare informazioni e scenari di progetto; identificare problemi, rischi e possibili alternative; prendere e motivare decisioni di progetto;
presentando le proprie conclusioni e raccomandazioni;
Le attività saranno svolte prevalentemente durante le lezioni, favorendo la partecipazione individuale, il confronto tra gli studenti e l'applicazione immediata dei concetti teorici.

Gestione dei progetti: generalità e storia della disciplina,le fasi della gestione dei progetti , agilità e disciplina nella gestione dei progetti, la gestione dei progetti complessi in ambito industriale, best practice globali, esercizi.

Conoscenze quantitative di base applicabili al Project Management, incluse aritmetica, percentuali, algebra di base, probabilità e statistica, con conoscenza della distribuzione normale

Lezioni frontali ed esercitazioni

Il metodo di verifica si basa su 2 parti: scritta e orale. La prova scritta consiste in un test sugli argomenti presentati e discussi a lezione La prova orale seguirà la prova scritta e consisterà in una discussione che, partendo dal progetto specifico, spaziera' tra i vari temi trattati durante il corso, ma anche casi di progetto ove valuare la capacita' dello studente nel collegare le varie aree di competetenza del project management. Saranno previsti test in itinere. Valutazione Voto Finale

1) Materiale a cura del docente (verrà distribuito durante il corso) Altri testi saranno segnalati durante il corso

By the end of the course, students will have a solid grounding in probability and estimation theory, learning to model noise and assess estimator quality through concepts such as bias, consistency and efficiency. Building on this, they will characterize stochastic processes and time series in the time and frequency domain, deriving models through spectral factorization and prediction.
These tools converge in the practical core of the course: identifying parametric models of linear time-invariant systems, estimating parameters and validating results through residual analysis. Students will also learn to design informative experiments, completing the full identification loop from experiment design to a validated model.

Recall on random variables: CDF/PDF, multivariate distributions, mean/variance, confidence intervals, covariance and correlation, Gaussian variables, functions of random variables, conditional distributions.
Estimation Theory: parametric estimation, estimator properties, performance criteria, Minimum Variance Unbiased Estimator, Cramér–Rao bound, Maximum Likelihood Estimation, nonlinear/linear estimation problems.
Time Series Analysis and Prediction: stochastic processes, key examples (deterministic, white noise, Wiener, exponentially correlated), frequency analysis, linear stochastic systems, MA/AR/ARMA models, ergodicity, spectral factorization, time series prediction.
Parametric Identification: LTI model structures (ARX, ARMAX, OE, BJ), cost function and model selection criteria, least squares estimation, numerical optimization, recursive identification (RLS), model validation (residual analysis), input design.
Use of software tools for system identification (e.g., MATLAB System Identification Toolbox). Extensive laboratory sessions and practical applications under instructor guidance.

Basic probability and statistics, linear algebra, signals and systems (Laplace/Z-transform), elementary control theory.

In-class time is structured as follows:

• Lectures, covering the theoretical and methodological foundations of estimation theory and system identification, complemented by in-class exercises (around 65% of the hours).
• Lab tutorials, providing hands-on practice in identifying and validating mathematical models of dynamic systems (around 35% of the hours).
  Out-of-class work consists of:
• Individual study of theoretical content and exercises.
• Homework assignments, to be completed individually.

The exam consists of two parts:

• a homework assignment;
• an oral exam.
  The homework requires students to solve an exercise individually and to write a report in LaTeX, clearly and concisely documenting the methodology adopted and the results obtained. A passing grade on the homework is required before students can take the oral exam.
  The oral exam consists of a discussion of the homework, together with questions covering all topics addressed during the course, including simple exercises.

• Lecture notes provided by the instructor
• A. Papoulis, S.U. Pillai: Probability, random variables, and stochastic processes (4th edition), McGraw-Hill, 2002
• T. Soderstrom, Discrete-time stochastic systems : estimation and control, Springer, 2002.
• L. Ljung, System Identification: Theory for the User (2nd edition), Prentice-Hall, 1999.
• L. Ljung. System Identification Toolbox: Getting started, The Mathworks, 2001.
• L. Ljung. System Identification Toolbox: User's guide, The Mathworks, 2001.