Objectives 4 Perceptron Learning Rule
Objectives4-1 4 4 Perceptron Learning Rule Objectives4-1Theory and Examples4-2Learning Rules4-2Perceptron Architecture4-3Single-Neuron Perceptron4-5Multiple-Neuron Perceptron4-8Perceptron Learning Rule4-8Test Problem4-9Constructing Learning Rules4-10Unified Learning Rule4-12Training Multiple-Neuron Perceptrons4-13Proof of Convergence4-15Notation4-15Proof4-16Limi tations4-18Summary of Results4-20Solved Problems4-21Epilogue4-33Further Reading4-34Exercises4-36 Objectives One of the questions we raised in Chapter 3 was: How do we determine the weight matrix and bias for Perceptron networks with many inputs, where it is impossible to visualize the decision boundaries?
Before we present the perceptron learning rule, letÕs expand our investiga-tion of the perceptron network, which we began in Chapter 3. The general perceptron network is shown in Figure 4.1. The output of the network is given by. (4.2) (Note that in Chapter 3 we used the transfer function, instead of hardlim
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