Monday 15 Sep 9 – 16 Malardalen Univ, Vasteras room S3-902
Tuesday 16 Sep 9 – 16 Malardalen Univ, Vasteras room R1-218
Wednesday 17 Sep 9 – 15 Malardalen Univ, Vasteras room S3-908
Lecturer:
Dr. Hans Georg Zimmermann
Senior Principal Research Scientist
Siemens AG, Corporate Technology, Munich
Course
content:
0 Introduction to Neural Networks
1 Neural Algorithms: More than the Numerics of Gradient Computation
2 Feedforward Neural Networks: More than Function Approximation
3 Model Building: More than Learning from Data
4 Neuro - Fuzzy: More than Neuro & Fuzzy
5 Recurrent Neural Networks: More than Algorithms
6 Open Systems: More than a Superposition of Internal & External
Dynamics
7 Error Correction Neural Networks: More than Autoregressive Modeling
8 Variance-Invariance Separation: More than Dimensionality Reduction
9 Unfolding in Space and Time: More than Unfolding in Time
10 Time in Time Series Analysis: More than Data Time
11 Stochastic Modeling: More than Deterministic Forecasting
12 Causal-Retro-Causal Networks: More than Causal Networks
13 Online Learning: More than Plasticity versus Stability
14 Large Networks: More than Increasing Dimensionality
15 Decision Support Systems: More than Forecasting
16 Multi-Agent Market Modeling: More than Econometrics
In total, the lecture contains about 300 slides.
The part about feedforward networks is at least partly covered in the
book chapter 'How to Train Neural Networks'.
The part about recurrent networks is partly covered in the book chapter
'Modeling of Dynamical Systems by Error Correction Neural Networks'.
For Portfolio Optimization see: 'Active Portfolio Management based on
Error Correction Neural Networks' and 'Optimal Asset Allocation for a
Large Number of Investment Oportunities'.
Concerning Undershooting see: "Undershooting: Modeling Dynamical Systems
by Time Grid Refinements' and concerning causal-retro-causal networks
see: 'Prosody Generation by Causal-Retro-Causal Error Correction Neural
Networks'.
Concerning Neuro-Fuzzy see: 'Neuro-Fuzzy Systems for Data Analysis'.
The multi-agent part is best covered in the Ph.D. thesis of my colleague
Ralph Grothmann. This thesis refers also to the main parts of the
analytical sections of the lecture. The thesis can be downloaded from:
http://elib.suub.uni-bremen.de/publications/dissertations/E-Diss437_grothmann.pdf
CV Hans Georg Zimmerman, lecturer during the course :
Study of mathematics, computer science and operations research in
Bonn, diploma 1982 in mathematics. Research in applications of control
theory in economics at the University of Bonn until 1987, PhD 1987 in
economics. Since 1987 at the department for Corporate Technology,
Siemens AG. Research in circuit simulation, since 1988 in neural
networks. Current research interests: Optimization, time series analysis
and economic aplications of neural networks. Since 1990 leader of the
project group 'Complex Systems Analysis by Neural Networks'. Head of
the SENN development (Simulation Environment for Neural Networks).
Work in the development of feedforward, recurrent and neurofuzzy network
architectures and algorthms for the modeling of economical dynamical
systems.
Participation/Registration:
If you want to participate, please send your name, affiliation and e-mail address to
Jan-Erik Käck jan-erik.kack@mdh.se . Please send your registration before Sept 8, for planning reasons.
Everyone interested is welcome to participate. The fee will be 300 SEK (or 30 Euro) per participant and can be paid directly on site or be added to the other fee for those participating in the SIMS2003. The fee will not cover any meals.