10. The ADALINE learning algorithm Step 0 Initialize all weights and set learning rate w ij = (small random values) = 0.2 (for example) Step 1 While stopping condition is false Step 1.1 For each training pair s:t : Step 1.1.1 Set activations on input units x j = s j Step 1.1.2 Compute net input to output units y_in i = b i + x j w ij Step 1.1.3 Update bias and weights b i (new) = b i (old) + ( t i – y_in i ) w ij (new) = w ij (old) + ( t i – y_in i ) x j
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14. Error Analysis The mean square error for the ADALINE Network is a quadratic function:
15. Adaptive Filtering Tapped Delay Line Adaptive Filter An adaptive filter is a filter that self-adjusts its transfer function according to an optimizing algorithm. Because of the complexity of the optimizing algorithms, most adaptive filters are digital filters that perform digital signal processing and adapt their performance based on the input signal.
42. Madaline Rule lI (MRI) training algorithm. High-level structure of a Madaline 11 with two Adalines at the first level and one Adaline at the second level. The Madaline Il architecture, shown in figure 4.3, improves on the capabilities of Madaline I, by using Adalines with modifiable weights at the Output layer of the network, instead of fixed logic devices. Figure 4.3
Next, state the action step. Make your action step specific, clear and brief. Be sure you can visualize your audience taking the action. If you can’t, they can’t either. Be confident when you state the action step, and you will be more likely to motivate the audience to action.
