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



The used NN are listed in table 1.

_______________________Set 1_______________________

_____________________Set 2_____________________

Topology

Order

Learning Law

Topology

Order

Learning Law

FF

Bm

FF

Bm

FF

Bp

FF

Bp

FF

_____Sn_____

Self

DA

Bp

Self ~

DA

Bp

Self

DA

Bm

Self

SA

Bm

Self

SA

Bp

Tasm

DA

Bm

Tasm

DA

Bm

Tasm

DA

Bp

Tasm

DA

Bp

Tasm

SA

Bm

Tasm

SA

Bm

Tasm

SA

Bp

Tasm

SA

Bp

Tasm

SA

Cm

Tasm

SA

_____Sn_____

Learning Law:            Bp = Back Propagation (standard)

Sn = Sine Net (Semeion)
Bm = Bi-Modal Network (Semeion)
Cm = Contractive Map (Semeion)

Topology:               FF = Feed Forward (standard)

Self = Self Recurrent Network (Semeion)
Tasm = Temporal Associative Subjective Memory (Semeion)

Order:                  DA = Dynamic and Adaptive Recurrency (Semeion)

_____________________________SA = Static and Adaptive Recurrency (Semeion____________________

Table 1 - The different architectures of SANN

7. Learning and validation of the SANNs

The Statistical functions used to evaluate the results are presented in Annexe 2. Each
function measures, separately, the error of each output vector component of SANNs
related to the correspondent Target value given in Input.

The first evaluation of the results is given by the statistical functions in table 2.

Residential

Industrial

Commercial

Average

RMSE

0.06756

0.05543

0.03290

0.09338

Real Error

-0.00262

-0.00938

-0.00550

-0.00583

Relative Error

0.05983

0.04911

0.01867

0.04254

Error Variance

0.11412

0.09735

0.05214

0.08787

NMSE

0.15737

0.22162

0.38873

0.25591

Squared R

0.84310

0.78374

0.62216

0.74967

Linear Corr.

0.91820

0.88529

0.78877

0.86409

Table 2 - Statistical measures of validation

12



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