3453950
This presentation is the property of its rightful owner.
Sponsored Links
1 / 78

مباحث : PowerPoint PPT Presentation


  • 200 Views
  • Uploaded on
  • Presentation posted in: General

مباحث :. معرفی شبکه های عصبی مصنوعی( ANN ها) مبانی شبکه های عصبی مصنوعی توپولوژی شبکه فرآیند یادگیری شبکه تجزیه و تحلیل داده ها توسط شبکه های عصبی مصنوعی ایده ی اصلی شبکه های عصبی مصنوعی معایب شبکه های عصبی مصنوعی کاربردهای شبکه های عصبی مصنوعی. مقدمه: زمان پاسخ گویی نرون طبیعی :

Download Presentation

مباحث :

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


3453950

:

(ANN)


3453950

:

:

:

.


3453950

ANN

  • .


3453950

ANN

  • .


3453950

ANN

  • .


3453950


3453950

.


3453950


3453950

output 1

0.9

Input 1

0.3

0.78

output 2

Input 2

0.3

0.7

output 3

0.8

.


3453950

( ) . ( ) .

.

FeedForward topology

Recurrent topology


3453950

Input layer

Output layer

Hidden layer


3453950


3453950

  • .

  • . :


3453950

  • . .

  • - . .

  • .

  • . .

  • . .


3453950

.

(supervised)

(unsupervised)

(reinforcement)


3453950

Perceptron

. 1 -1 .


3453950

Linearly separable

+

+

+

+

+

-

-

-

+

-

-

-

Linearly separable

Non-linearly separable


3453950

bias

1 W0 .


3453950

http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg

  • :

  • :

if

otherwise

where

=

1 if y > 0

-1 otherwise


3453950

:

  • . .

  • :

    • 2


3453950

:

:


3453950

  • :

= ( t o ) xi

t: target output

o: output generated by the perceptron

: constant called the learning rate (e.g., 0.1)

.


3453950

Delta Rule

  • . .

  • gradient descent . Backpropagation.


3453950

Delta Rule

  • . . :


3453950

Delta Rule

  • :

    : learning rate (e.g., 0.1)


3453950

MultilayerArchitecture

Output

layer

Input

layer

Hidden Layers


3453950

1

-10 -8 -6 -4 -2 2 4 6 8 10

Activation Functions

Sigmoidal Function


3453950

Back propagation

  • Back Propagation . gradient descent .

  • :

outputs tkdokd k d .


3453950

Back-propagation Algorithm


3453950

(Forward Step)

X .

.


3453950

(Backward Step)

  • :

  • :

  • :

    :


3453950

BP

  • ninnhiddennout.

  • .

  • ) ( :

    x:

    X

    E .


3453950

BP

  • gradient descent .

  • :

    • stochastic gradient descent


3453950

  • n .

    0 <= <= 1.

    :

    • .


3453950

overfitting

  • BP

  • . overfitting.

Validation set error

Error

Training set error

Number of weight updates


3453950

overfitting

  • overfitting. .

  • .


3453950

  • Vallidation.

  • : weight decay.

  • k-fold cross validation m K k . . .


3453950

BP :

  • .

  • .

    Overfitting.


3453950

:

    • Hybrid Global Learning

    • Simulated Annealing

    • Genetic Algorithms

    • Radial Basis Functions

    • Recurrent Network


3453950

FNN

  • Stimulated Annealing

  • PSO Particle Swarm Optimization

  • ...


3453950

  • (posterior probability)


3453950

ANN

  • .

  • .

  • .

  • ( ) ..

  • . .


3453950

RBF

  • .


3453950


3453950

:


3453950

  • :

  • :

  • k


3453950

:

xi

f(xi) .

(f(xi yi . W

. .

4. .

5. .


3453950

  • (Pattern Recognition) (Character Recognition)

  • (Speech Recognition)

  • (Image Processing)

  • (Classification)


3453950

( ...)

  • /

  • /


3453950

  • .


3453950

( )

Failure mode and effects analysis

* * = RPN


3453950

  • RPN .

  • ( ) . .

  • .


3453950


3453950


3453950

Particle Swarm Optimization

  • (Evolutionary) .

  • Kennedy Eberhart 1995 .

  • .

  • PSO (Population) .

  • PSO .

  • PSO .


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Concept


3453950

  • (Pb) (Pg) .

  • (Pb) (Pg) .


3453950

Particle Swarm Optimization The Basic Model


3453950

Particle Swarm Optimization The Basic Model

Rules of movement

Vid(t+1)= Vid(t)+c1* rand()*[Pid(t)-xid(t)]+c2*rand()*[Pgd(t)-xid(t)]

Xid(t+1)=xid(t)+vid(t+1) 1i n 1 d D

c1 c2 rand() 0 1 .


3453950

Particle Swarm Optimization The Basic Model


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Concept

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

x2

max

x1

min

fitness

Particle Swarm Optimization Animation

search space


3453950

. . . .


Particle swarm optimization flow chart

Particle Swarm Optimization Flow Chart

Flow chart depicting the General PSO Algorithm:

Start

Initialize particles with random position

and velocity vectors.

For each particles position (p)

evaluate fitness

Loop until all particles exhaust

If fitness(p) better than

fitness(pbest) then pbest= p

Loop until max iter

Set best of pBests as gBest

Update particles velocity (eq. 1) and

position (eq. 3)

Stop: giving gBest, optimal solution.


3453950

:

www.rsh.ir

http://en.wikipedia.org/wiki/Neural_network

http://www.neuralnetworksolutions.com/resources.php

http://www.tandf.co.uk/journals/titles/0954898X.asp

http://www.30sharp.com

( )


  • Login