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Neuron Model - Banking - Lecture Slides, Slides of Banking and Finance

Banking is an ever green field of study. In these slides of Banking, the Lecturer has discussed following important points : Neuron Model, Network Architectures, Input Neuron, General Neuron, Transfer Functions, Linear Transfer Function, Limit Transfer Function, Layer of Neurons, Abbreviated Notation, Multilayer Network

Typology: Slides

2012/2013

Uploaded on 07/29/2013

sathyanna
sathyanna 🇮🇳

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103 documents

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Download Neuron Model - Banking - Lecture Slides and more Slides Banking and Finance in PDF only on Docsity!

Neuron Model

and

Network Architectures

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a

f ( wp

b

)

General Neuron

a

n

Inputs

AA

b

p

w

AAAA

f

Single-Input Neuron

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Transfer Functions

1

1

n

0

b/w

p

0

AAAA

a = logsig

(^) (n)

Log-Sigmoid Transfer Function

a = logsig

(^) (wp

(^) b)

Single-Input

logsig

Neuron

a

a

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Multiple-Input Neuron

Multiple-Input Neuron

p

1

a

n

Inputs

b

p

2

p

3

p

R

w

1, (^) R

w

1, (^1)

AAAA

a

f (^) ( Wp

(^) b

)

AAAA

f

AAAAAA

f

Multiple-Input Neuron

a

f (^) ( Wp

(^) b

)

p

a

n

AA

W

AAAA

b

R (^) x (^1)

1 (^) x (^) R

1 (^) x (^1)

1 (^) x (^1)

1 (^) x (^1)

Input

R

Abreviated Notation

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Abbreviated Notation

AAAAAA

f

Layer of

S

Neurons

a

f ( Wp

(^) b

p

a

n

A

W

AA

b

R (^) x (^1)

S (^) x (^) R

S (^) x (^1)

S (^) x (^1)

S (^) x (^1)

Input

R

S

W

w

1 1 ,

w

1 2 ,

w

1

R

,

w

2 1 ,

w

2 2 ,

w

2

R

,

w

S^

1

,

w

S

2

,

w

S R^ ,

=

b

S 2 1

=

b b b

p

p 1

p 2

p

R^

=

a

a 1

a 2

a S

=

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Multilayer Network

First Layer

a 1 =

f 1 (^ W

1 p (^) +

(^) b 1 ) a 2 = f 2

(^ W

2 a 1 +^ (^) b

2 ) a 3 = f 3

(^ W

3 a 2 +^ (^) b

3 )

AAAA

f 1

AAAA

f 2

AA

f 3

Inputs

a 3 2

n 3 2

w

3 S (^) 3 , S (^) 2

w

3 1,

b 3 2

b 3 1

b 3 S (^) 3

a 3 S (^) 3

n 3 S (^) 3

a 3 1

n 3 1

1 1 1

1 1 1

1 1 1

p 1

a 1 2

n 1 2

p 2

p 3

p R

w

1 S 1 , R

w

1 1,

a 1 S (^) 1

n 1 S (^) 1

a 1 1

n 1 1

a 2 2

n 2 2

w

2 S 2 , S

1

w

2 1,

b 1 2

b 1 1

b 1 S (^) 1

b 2 2

b 2 1

b 2 S (^) 2

a 2 S (^) 2

n 2 S 2

a 2 1

n 2 1

AAAA

Σ

AA

Σ

AAAA

Σ

AAAA

Σ

AA

Σ

AAAA

Σ

AAAA

Σ

AA

Σ

AAAA

Σ

AA

f 1

AAAA

f 1

AAAA

f 2

AAAA

f 2

A

f 3

AA

f 3

a 3 =

f 3 (^ W

3 f 2 (^ W

2 f 1 (^ W

1 p (^) +

(^) b 1 ) (^) +

(^) b

2 ) (^) +

(^) b

3 )

Third Layer

Second Layer

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Delays and Integrators

AAAA

D

a

( t )

u

( t )

a

(0)

a

( t ) =

u

( t (^) -

Delay

a

( t )

a

(0)

Integrator

u

( t )

a

( t ) =

u ( τ ) d τ + a

0 t

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Recurrent Network

Sym. Sat. Linear Layer

AA A

R (^) x (^1)

S (^) x (^) R

S (^) x (^1)

S (^) x (^1)

S (^) x (^1)

Initial

Condition

p

a

( t (^) +

n

( t (^) +

W

b

S

S

AAAA

D

AAAAAA

a

( t )

a

(0)

p

a

( t (^) +

satlin

Wa

t ) (^) +

(^) b

S (^) x (^1)

a

2

(

)

satlins Wa

1

(

)

b

(

)

=

a

1

(

)

satlins Wa

0

(

)

b

(

)

satlins Wp

b

(

)

=

=

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