Neural NetworksLanguage 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.

Neural Network

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.

Motive

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

Grammar Used

// 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 neuronsoma s2 Triangle(3);dendrite d3 Value(1);dendrite d4 Rand(1,2);neuron n2 s2 d3 d4 ;

Sample Code

// create second neuronsoma s3 Triangle(3);dendrite d5 value(4);dendrite d6 Rand(1,2);neuron n3 s3 d5 d6;

// connect neuronssynapse 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

Parts of Language

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

Features

Hardware Requirements

Software Requirements

Concurrency Issues◦ Simulated currency by processing layers

Future Enhancements

Questions