xMQL5/BKPS/14030412/Documents/NN/Documents.md

Concepts of a Neural Network (NN)

simulation of human nervous system in its ability to learn and adpt.

Units

1. Inputs
2. Weights
3. Transfer Function + net output
4. Activation Function
5. Outputs

Flow

1. Update and Provide our Inputs;
2. Transfer Function;
3. Net OutPut (Inputs and Weights);
4. Actiation Function;
5. Calculate Output;

Learnign

it is a process to Change Weights of inputs based on results ... this happens to algorithm improve itself optimization ...

Net

in this process Inputs and their Weights collecting together.

Activation Function

Recieved Net Inputs (Weighted Inputs) and then Calculate output based on them.

Multi Layer NN

1. Input Layer;
2. Hidden Layer;
3. Output Layer;

Hidden Layers recieved Data from all other layers at the end, then Populate as a Neuron.

Input Data Normalization

a Process where all the input data normalized. reduce data to an accepted ranges. i.e. [0,1] or [-1, 1].

this peocess so important for us for making data more acceptable.

this can done by some standard form:

y = x - (x(min) * (d2 -d1)) / x(max - x(min)) + d1

x => value to normalized; x(min)/x(max) => x Value range max and min; d1,d2 => ranges to upper and lower normalization;

Activation Functions

a function which calculate the output of a neuron. it recieved a Net Input (Weighted functions);

1. Unit Step / Hard Threshold Functions;
2. Sigmoid Function;
3. Hyperbolic Tangent Function;

Unti Step

a Graph by y axix Output and x axix Net Input and values in y between 0, 1 and x is 0 to TETA or threshold.

Sigmoid

Change Activation Functions Shape;