Chap 8: Adaptive Networks 2019/1/2 Neural Networks Chap 8: Adaptive Networks ... 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.

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

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

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

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.)