How ANN model deals with missing input data?
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I am new to matlab ANN, It will be very helpfull if somone can calrify these queries :
1) How ANN model deals with missing input data?
2) what will happen if I use my 100% data in training model ?
3) How to find out suitable no of layers
4) Why ANN model gives different results (R value) if I am training it again and again with the same input data
5) Can I input my data in sd form such as 185 ± 6
6) How to do process optimizatin using ANN model
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Walter Roberson
2023-11-24
2: If you use 100% of your data in training mode, you would typically end up with indexing errors or array-size-mismatch errors when it tried to construct the data to use in the test phase.
4: ANN training usually initializes the weights randomly. The whole process of ANN training involves generating a random set of initial weights, iterating to find better weights, recording the outcomes -- and then trying again and again... and eventually take the version that led to the best outcome.
5: No, data cannot be entered in the form of a ±
Sunita
2023-11-24
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Pratyush
2023-11-24
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Hi Sunita,
I understand that you have a couple of queries on MATLAB Artificial Neural Network. Here are the answers in sequential order to your queries:
- Missing data can be handled by imputing values using mean, median, or advanced methods like KNN imputation.
- Using all data for training can lead to overfitting. Split your data into training and testing sets for better generalization.
- Experiment with different numbers of layers, starting simple and gradually increasing complexity.
- Results can vary due to random weight initialization. Set a specific random seed for reproducibility.
- Yes, you can input data in SD form, but preprocess it to a format suitable for the neural network, such as normalization.
- Train the network to minimize or maximize an objective function using techniques like backpropagation, genetic algorithms, or particle swarm optimization in MATLAB.
Hope this helps.
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