1. Chap 8: Adaptive Networks
2019/1/2
Neural Networks
Chap 8: Adaptive Networks
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In this talk, we are going to apply two neural network controller design techniques to fuzzy controllers, and construct the so-called on-line adaptive neuro-fuzzy controllers for nonlinear control systems. We are going to use MATLAB, SIMULINK and Handle Graphics to demonstrate the concept. So you can also get a preview of some of the features of the Fuzzy Logic Toolbox, or FLT, version 2.

2. Neural Networks Supervised Learning Unsupervised Learning Others
2019/1/2
Neural Networks
Supervised Learning
Multilayer perceptrons
Radial basis function networks
Modular neural networks
LVQ (learning vector quantization)
Unsupervised Learning
Competitive learning networks
Kohonen self-organizing networks
ART (adaptive resonant theory)
Others
Hopfield networks

3. Adaptive Networks Architecture: Goal: Basic training method: x z y
2019/1/2
Adaptive Networks x z y
Architecture:
Feedforward networks with diff. node functions
Squares: nodes with parameters
Circles: nodes without parameters
Goal:
To achieve an I/O mapping specified by training data
Basic training method:
Backpropagation or steepest descent

4. Single-Layer Perceptrons
2019/1/2
Single-Layer Perceptrons
Network architecture x1 w1 w0 w2 x2
y = signum(Swi xi + w0) w3 x3
Dwi = k t xi
Learning rule

5. Single-Layer Perceptrons
2019/1/2
Single-Layer Perceptrons
Example: Gender classification h v w1 w2 w0
Network Arch.
y = signum(hw1+vw2+w0)
-1 if female
1 if male = y
Training data
h (hair length)
v (voice freq.)