神经元模型和网络结构课件（英文）.ppt

1.Objectives 2-1 2.Theory and Examples 2-2 Notation 2-2 Neuron Model 2-2 Single-Input Neuron 2-2 Transfer Functions 2-3 Multiple-Input Neuron 2-7 Network Architectures 2-9 A Layer of Neurons 2-9 Multiple Layers of Neurons 2-10 Recurrent Networks 2-13 3.Summary of Results 2-16 Solved Problems Single-Input Neuron Weight , Bias,Net Input,Transfer Function A single-input neuron is shown in Figure 2.1. The scalar input p is multiplied by the scalar weight w to form wp, one of the terms that is sent to the summer. The other input, 1 , is multiplied by a bias b and then passed to the summer. The summer output n, often referred to as the net input, goes into a transfer function f , which produces the scalar neuron output a. (Some authors use the term “activation function” rather than transfer function and”offset” rather than bias.) Single-Input Neuron(图2-1） The neuron output is calculated as .a=f(wp+b) If, for instance, w= 3 , p =2, b= –1.5 , and , then a= f(3 ( 2) – 1.5)=f ( 4.5) Transfer Functions/1 The transfer function in Figure 2.1 may be a linear or a nonlinear function of n . A particular transfer function is chosen to satisfy some specification of the problem that the neuron is attempting to solve. Transfer Functions/2 Transfer Functions/3 Transfer Functions/4 Most of the transfer functions used in this book are summarized in Table 2.1. Of course, you can define other transfer functions in addition to those shown in Table 2.1 if you wish. To experiment with a single-input neuron, use the Neural Network Design Demonstration One-Input Neuron nnd2n1. Transfer Functions/5 Transfer Functions/6 Multiple-Input Neuron/1 Multiple-Input Neuron/2 To experiment with a two-input neuron, use the Neural Network Design Demonstration Two-Input Neuron (nnd2n2). Layer of Neurons/1 Each element of the input vector p is connected to each neuron through the weight matrix W. Each neuron has a bias b i , a summer, a transfer function f and an output a i. Taken together, the outp