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Kamuran Turksoy


Last seen: 3 years 前 自 2012 起处于活动状态

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Is it possible to change some properties of only a subset of units (neurons) in a hidden layer?
I am wondering if it is possible to change some properties of only a subset of units (neurons) in a hidden layer? Let say you ha...

6 years 前 | 1 个回答 | 0

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提问


Multi Input Multi Sequence Neural Network
The question is how to define a multi input multi sequence neural network (NN) in Matlab? There is a way to define multi sequ...

6 years 前 | 1 个回答 | 0

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已回答
How can I set the normalization of the performance parameter in training a neural network?
net.performParam.normalization does not have an option of 'normalized'. It has three options: 'none' (default: no normaliza...

6 years 前 | 1

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提问


Why neural network sometimes performs worse when additional input variables are used?
Why neural network sometimes performs worse when additional input variables are used? I would expect a neural network traine...

7 years 前 | 1 个回答 | 0

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Selection of Neural Network Training Data
One can divide his/her data into training, validation and testing and use them to train a neural network model (regression in my...

7 years 前 | 1 个回答 | 0

1

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已回答
How to change the performance function of neural network to mean absolute relative error
I found my own answer for the performance function myperf = mse(1-y./t) as follows: net.performFcn='mse'; % this is the defau...

7 years 前 | 0

| 已接受

提问


How to change the performance function of neural network to mean absolute relative error
Hello, I know the Matlab NN toolbox has MSE, SSE, MAE and SAE performance functions but would like to implement a custom perf...

7 years 前 | 2 个回答 | 0

2

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Single PLS model based on multiple datasets
Is there any way to train a single PLS model on several training datasets? Let X1, X2 and X3 be the predictive variables mat...

7 years 前 | 0 个回答 | 0

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Time Series Normalization??
If you have multiple inputs, and the amplitudes of your inputs are different then it is better to normalize your inputs. In othe...

12 years 前 | 0