1 / 18

Neuron Model and Network Architectures

Neuron Model and Network Architectures. Biological Inspiration. 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. Notation. Scalars-small italic letters ： a,b,c Vectors-small bold nonitalic letters ： a,b,c

arav
Télécharger la présentation

Neuron Model and Network Architectures

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Neuron Model and Network Architectures

  2. Biological Inspiration

  3. Neuron Model a1~an為輸入向量的各個分量 w1~wn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. 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. Single-Input Neuron 例1：w=3,p=2 and b=-1.5then a=f(3(2)-1.5)=f(4.5)

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

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

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

  9. Transfer Functions

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

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

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

  14. Abbreviated Notation w w ¼ w 1 , 1 1 , 2 1 , R w w ¼ w W 2 , 1 2 , 2 2 , R = w w ¼ w S , 1 S , 2 S , R p b a 1 1 1 p b a p a b 2 2 2 = = = p b a R S S

  15. Multiple Layers of Neurons Three-Layer Network

  16. Abbreviated Notation Hidden Layers Output Layer

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

  18. Recurrent Network

More Related