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|Notices (updated 19.10.2005)
* We will start the lectures strictly at 14.00.
The change is valid for the whole semester.
* Information and description of the project is added
on the web page (see. section Project work).
* The project work has been changed.
You can find the description of the project from the section Project work.
|Lecturers||Prof. (pro tem) Jaakko Hollmén,|
PhD (Eng.) Amaury Lendasse
|Assistant||M.Sc. Jarkko Tikka|
|Semester||Autumn 2005 (during periods I and II)|
|Seminar sessions|| On Wednesdays at 14-16 in lecture hall T4 in computer science building,|
Konemiehentie 2, Otaniemi, Espoo. The first session on 14.9.2005.
|tikka (at) mail.cis.hut.fi|
The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This course provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the student with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank.
2. System Identification with Neural Networks
3. Control with Neural Networks
Detailed requirements are given in the first lecture.
Neural Networks for Modelling and Control of Dynamic Systems
Magnus Nørgaard, Ole Ravn, Niels K. Poulsen and Lars K. Hansen
Springer-Verlag, London, 2000
(68 EUR in AKATEEMINEN KIRJAKAUPPA)
|14.9.||Amaury Lendasse||Presentation of the course|
|Introduction to ANN and System Identification p.1-18 + paper 1, Esa + Elia|
|Model Structure Selection p.18-37 + paper 1, Vibhor + Sven|
|Experiments and Determination of Weights p.38-84, Yoan + Kei|
|Validation Procedure and Summary p.85-119, Rami + Ali|
|26.10||-||Project (no lecture)|
|Introduction + Direct Inverse Control + IMC p.121-142, Mikael+Janne|
|Feedback Linearization + Feedforward Control + Optimal Control + CBIL p. 143-175, Antti+Eemeli|
|Predictive Control + Recapitulation + Case Study p. 178-233|
|30.11||Presentations of projects (20 minutes per group)|
Nonlinear Black-Box Modeling in System Identification: A Unified Overview
Jonas Sjöberg, Qinghua Zhang, Lennart Ljung, Albert Benveniste, Bernard Delyon, Pierre-Yves Glorennec, Håkan Hjalmarsson and Anatoli Juditsky
Automatica, Volume 31, Issue 12, Pages 1691-1724, Elsevier, December 1995
The project by group of 2 or 3 students during weeks 6 to 8 IN THE CLASSROOM.
For more information, please send email to the course assistant (tikka (at) mail.cis.hut.fi).
You are at: CIS → /Opinnot/T-61.6050/s2005/index.shtml
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