A Neural Network produced by EasyNN-plus has two component parts.

 

The component parts are the Node and the Connection.  These components are replicated to make the neural network.  A Node consists of a Neuron with positioning and connecting information. A Connection consists of a Weight with node addressing information.

 

The Grid used by EasyNN-plus also has two components.  These are the Example row and the Input/Output column.  These are replicated to make the grid.

 

 

All the component parts of EasyNN-plus are implemented as reusable classes to simplify future development.  The following information is a very basic description of the classes. The true names of the variables and functions are not used.

 

 

The Neural Network    

 

    Node    

 

          Positioning and connection

   

          Type:

              Input, Output or Hidden.

 

          Number:

              Node reference number.

 

          Layer Type:

              Input, Output or Hidden.

 

          First In:

              Number of the first connection into this node.

 

          Last In:

              Number of the last connection into this node.

 

          Neuron    

 

          Net Input:

              Sum of all activation * weight inputs to the node + Bias.

 

          Activation:

              1.0 / (1.0 + e (-Net Input))

 

          Output Node Error:

              Target - Activation

 

          Hidden Node Error:

              Error + Delta * Weight

 

          Delta:

              Error * Activation * (1.0 - Activation)

 

          Bias:

              Bias + Delta Bias

 

          Bias Derivative:

              Bias derivative + Delta

 

          Delta Bias:

              Learning Rate * Bias Derivative + Momentum * Delta Bias

 

    Connection    

 

          Node addressing    

 

          To:

              The Node that the connection is going to.

 

          From:

              Node that the connection is coming from.

 

          Number:

              Connection reference number.

 

    Weight    

 

          Type:

              Variable or Fixed.

 

          Weight:

              Weight + Delta Weight.

 

          Weight Derivative:

              Weight Derivative + To: Delta * From: Activation.

 

          Delta Weight:

              Learning Rate * Weight Derivative + Momentum * Delta Weight.

 

The Grid

   

    Example row

   

    Name:

          Optional Example name.

 

    Type:

          Training, Validating, Querying or Exclude.

 

    Values:

          Array of values in example row.

 

    Scaled Values:

          Array of scaled values in example row.

 

    Forecasted Values:

          Array of forecasted values in example row.

 

    Input/Output column

   

    Name:

          Optional Input/Output name.

 

    Type:

          Input, Output, Serial or Exclude.

 

    Mode:

          Real, Integer, Bool, Text or Image.

 

    Lock:

          True or False.

 

    Lowest:

          Lowest value in column.

 

    Highest:

          Highest value in column.