Seminar Course on "Neural Networks for Nonlinear System Identification"

Frank Hoffmann (fhoffman@cs.berkeley.edu)
Mon, 13 Sep 1999 12:37:20 +0200 (MET DST)

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Berkeley Initiative in Soft Computing (BISC)
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>From nelles@mechatro2.me.berkeley.edu Fri Sep 10 10:48 PDT 1999
Subject: Seminar Course on "Neural Networks for Nonlinear System Identification"

Seminar Course on "Neural Networks for Nonlinear System Identification"

Starting September 15, 1999, I will offer a new seminar course ME 298
on neural networks for nonlinear system identification. It will cover an

introduction to fuzzy/neuro-fuzzy systems too. The emphasis will be on
intuitive understanding of the topics and many discussions. Some
background in linear system identification is useful but no
prerequisite.

ME 298: Neural Networks for Nonlinear System Identification
Wednesdays 3-5pm, (for 10 weeks),
C337 Cheit (in the Haas Business Complex)
CNN: 56402, Section 11
Starting Sept. 15, 1999

1. Introduction
1.1 Approximation of Static Systems
1.2 Identification of Dynamic Systems
1.3 Clustering and Classification

2. Parameter Optimization
2.1 Linear Optimization
2.2 Nonlinear Optimization
2.3 How Flexible Should a Model Be?

3. Neural Network Architectures
3.1 What is a Neural Network?
3.2 History and Overview
3.3 Multi-Layer Perceptron (MLP) Network
3.4 Radial Basis Function (RBF) Network
3.5 Neuro-Fuzzy (NF) Networks
3.6 Brief Overview on Other Architectures

4. Review of Linear System Identification

5. External Dynamics
5.1 Prediction and Simulation
5.2 Models with Output Feedback
5.3 Models without Output Feedback
5.4 Suitability of Neural Network Architectures

6. Internal Dynamics
6.1 Fully Recurrent Networks
6.2 Partially Recurrent Networks
6.3 Locally Recurrent Globally Feedforward Networks

7. Examples and Applications

Dependent on the interests of the participants, this seminar can
additionally cover some of the following topics in greater detail:
- Fuzzy Systems
- Evolutionary Algorithms
- The Role of Prior Knowledge
- Regularization Techniques
- Orthonormal Basis Functions (Laguerre & Kautz Filters)

With kind regards,
Oliver

========================================
Dr.-Ing. Oliver Nelles

Department of Mechanical Engineering
University of California at Berkeley
6189 Etcheverry Hall #1740
Berkeley, CA 94720-1740
USA
Phone: +1 (510) 642-0935
Fax: +1 (510) 642-6163
e-mail: nelles@mechatro2.me.berkeley.edu

Private Address:
211 Purdue Ave.
Berkeley, CA 94708
USA
Phone: +1 (510) 558-1863

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Frank Hoffmann UC Berkeley
Computer Science Division Department of EECS
Email: fhoffman@cs.berkeley.edu phone: 1-510-642-8282
URL: http://http.cs.berkeley.edu/~fhoffman fax: 1-510-642-5775
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