Fitted Parameters & Mathematical Equation for ANN and ANFIS
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I am trying to figure out how I can obtain the number of fitted parameters and the mathematical equation for both ANN and ANFIS models.
Neural Network Model:
- Feed-Forward Multi-layer Perceptron network with Back-Propagation
- Learning/Training Algorithm: Levenberg-Marquardt
- Transfer function(Hidden layer): Tan-Sig
- Transfer function(Output): Linear
- X's (inputs): 9 inputs
- Y (response): 1 response
- Number of nodes in the hidden layer: 15
- Best Validation performance obtained at epoch 7
Fuzzy Logic (2models):
- X's (inputs): 5 inputs
- Y (response): 1 response
- Subclustering: (Range of influence/radius=[0.5,0.6] - Squash factor [1000-2000] - Accept/Reject Ratios=1.25 - 6 rules*)
- Grid Partitioning: (Input membership function type='trapmf' - defuzzification method='wtaver' - 32 rules*)
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回答 (1 件)
Greg Heath
2017 年 6 月 21 日
MULTILAYER PERCEPTRON (MLP)
[ I N ] = size(input)
[ O N ] = size(target)
[ O N ] = size(output)
Ntrn = N-2*round(0.15*N) % No. of training inputs
Ntrneq = Ntrn*O % No of training equations
H = number of hidden nodes (single hidden layer)
I-H-O = topology of a single hidden layer MLP
Nw = (I+1)*H + (H+1)*O % No. of unknown weights
Nw > Ntrneq ==> Overfitting ==> H > Hub
Hub = (Ntrneq-O)/(I+O+1) % ub => upper bound
NOTE: Overfitting can lead to unstable iterative solutions.
In particular, overtraining an overfit net can lead to
poor generalization (i.e., poor performance on nontraining
data).
OVERFITTING MITIGATION
1. Ntrneq >= Nw (Don't overfit!)
2. Validation subset stopping (MATLAB default)
3. Bayesian Regularization (via trainbr)
ANALYTIC SOLUTION
output = B2 + LW*tanh(B1+IW*inputs)
Search both the NEWSGROUP and ANSWERS for details
SEARCH HITS
SEARCHWORDS NEWSGROUP ANSWERS
B1 B2 IW LW 73 208
B1 B2 IW LW GREG 45 145
Hope this helps
Thank you for formally accepting my answer
Greg
2 件のコメント
Greg Heath
2017 年 6 月 25 日
Sorry, have no time for this now.
However, I have posted zillions of examples in both NEWSGROUP and ANSWERS.
Hope this helps.
Greg
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