The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Input Unit

Number of units in the input vector (12)

K Units

Number of units in the output matrix (from 9 to 49, depending on the simulation)

K Rows

Number of rows in the output matrix (from 3 to 7, depending on the simulation)

K Cols

Number of columns in the output matrix (from 3 to 7, depending on the simulation)

K Dimension

Output matrix dimensions (2)

K Topology

Output matrix space topology (Euclidean)

N Topology_________

Winner unit neighbourhood space topology (square)___________________________________

N function

Parameter defining the function to update the units connections in the WU neighbourhood
(Gaussian)____________________________________________________________________________

Alpha Max_________

Maximum width for the Nfunction (1)____________________________________________

Alpha Min__________

Minimum width for the Nfunction (0)_______________________________________________

Alpha Inc___________

Factor reducing Alpha Max in each epoch (0.01)______________________________________

Set Weight___________

Maximum weight value during the initialisation_________________________________________

Alpha W Func______

Input/output weights correction function (constant)________________________________________

Alpha W Max_______

Initial value of weight correction factor (0.1)_______________________________________________

Alpha WMin_______

Minimum value of weight correction factor (0)_______________________________________

Alpha W Inc________

Decreasing amount of the weight correction factor (0.001)_______________________________

Epochs

The epochs number for an experiment is automatically calculated by this formula:

A A AlphaMax - AlphaMin
Epochs =------------------

AlphaInc