Neural Networks pptx

runmidgeΤεχνίτη Νοημοσύνη και Ρομποτική

20 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

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Language Project


Neural networks have a large appeal to many
researchers due to their great closeness to the
structure of the brain, a characteristic not shared
by more traditional systems
.



In an analogy to the brain, an entity made up of
interconnected neurons, neural networks are
made up of interconnected processing elements
called units, which respond in parallel to a set of
input signals given to each. The unit is the
equivalent of its brain counterpart, the neuron.



A neural network consists of four main parts:


1. Processing
units.


2. Weighted interconnections between the
various processing units which determine how
the activation of one unit leads to input for
another unit.


3. An activation rule which acts on the set of
input signals at a unit to produce a new output
signal, or activation.


4. Optionally, a learning rule that specifies how
to adjust the weights for a given input/output
pair.




A
-
> O; A


| D; A | N ; A | Y; A | S ; A | R;A| e


O
-
> soma I F



--------
body of a neuron


E
-
>dendrite E


I
-
> id


Y
-
>synapse II



---------

a connection


F
-
>function P


P
-
>( P’)


P’
-
>Z,P’| Z


D
-
> dendrite I F

--------

input to neuron


N
-
> neuron E


---

a neuron composed of


soma and dendrite


S
-
>sense Z I


----

information is supplied to this node


R
-
> result Z
id
----

results are supplied to this node


Z
-
>
number




// create first
neuron (Logistic and Triangle are
functions)


soma s1 Logistic(10, 2, 5
);

dendrite d1 Value(1);

dendrite d2 Rand(1,2);

neuron n1 s1 d1 d2 ;


// create second neuron

soma s2 Triangle(3);

dendrite d3 Value(1);

dendrite d4 Rand(1,2);

neuron n2 s2 d3 d4 ;




// create second neuron

soma s3 Triangle(3);

dendrite d5 value(4);

dendrite d6 Rand(1,2);

neuron n3 s3 d5 d6;


// connect neurons

synapse n2 d2;

synapse n3 d1;


// input

sense 1 d1;

sense 2 d3;

sense 3 d4;


// out

result 1 n1;



Our Programming language constitutes of
three parts in general


1. Framework initialization


2. Topology implementation


3. Processing engine


Facilitates Back Propagation
-

With this feature
we can go back and trace the assign values of
the neurons

**** back propagation is used
for learning




Can
Determine two neurons
Train two or
more neural networks simultaneously
-
explanation






Concurrency Issues


Simulated currency by processing layers