1. 2 1 Neuron Model and Network Architectures

2. 2 2 Biological Inspiration

3. 2 3 Neuron Model a 1 ~a n 為輸入向量的各個分量 w 1 ~w n 為神經元各個突觸的權值 b 為偏差 f 為傳遞函數，通常為非線性函數。 例如： hardlim(n) ， n 正為 1 ，其餘 0 t 為神經元輸出

4. 2 4 Notation Scalars-small italic letters ： a,b,c Vectors-small bold nonitalic letters ： a,b,c Matrices-capital BOLD nonitalic letters ： A,B,C Input-p,p,P Weight-w,w,W Bias-b,b Output-a,a,a(t)

5. 2 5 Single-Input Neuron 例 1 ： w=3,p=2 and b=-1.5 then a=f(3(2)-1.5)=f(4.5)

6. 2 6 Transfer Functions 例 2 ： w=3, p=2 and b=-1.5 then a=hardlim(3(2)-1.5)=hardlim(4.5)=1 a=0 n<0 a=1 n>=0

7. 2 7 Transfer Functions 例 3 ： w=3, p=2 and b=-1.5 then a=purelin(3(2)-1.5)=purelin(4.5)=4.5

8. 2 8 Transfer Functions 例 4 ： w=3, p=2 and b=-1.5 then a=logsig(3(2)-1.5)=logsig(4.5)=

9. 2 9 Transfer Functions

10. 2 10 0<=a<=1 -1<=a<=1

11. 2 11 Multiple-Input Neuron Abbreviated Notation Neuron With R Inputs

12. 2 12 Example P2.3 Given a two-input neuron with the following parameters: b=1.2, W= [ 3 2 ] and p= [ -5 6 ] T, calculate the neuron output for the following transfer functions: i. A symmetrical hard limit transfer function ii. A saturating linear transfer function iii. A hyperbolic tangent sigmoid(tansig) transfer function i. a=hardlims(-1.8)= -1 ii. a=satlin(-1.8)= 0 iii. a=tansig(-1.8)=

13. 2 13 Layer of S Neurons R Input S Output i.e.,R≠S Layer of S Neurons

14. 2 14 Abbreviated Notation W w 11  w 12   w 1R  w 21  w 22   w 2R  w S1  w S2   w SR  = b 1 2 S = b b b p p 1 p 2 p R = a a 1 a 2 a S =

15. 2 15 Multiple Layers of Neurons Three-Layer Network

16. 2 16 Abbreviated Notation Hidden LayersOutput Layer

17. 2 17 Delays and Integrators a(0)=a(0) a(1)=u(0)

18. 2 18 Recurrent Network