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Performance evaluation of artificial neural networks for spatial data analysis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Moustafa, Akram A. AlQadi, Ziad Shahroury, Eyad |
| Copyright Year | 2011 |
| Abstract | the artificial neural network training algorithm is implemented in MATLAB language. This implementation is focused on the network parameters in order to get the optimal architecture of the network that means (the optimal neural network is the network that can reach the goals in minimum number of training iterations and minimum time of training). Many examples were tested and it was shown that using one hidden layer with number of neuron equal to the square of the number of inputs will lead to optimal neural network by mean of reducing the number of training stages (number of training iterations) and thus the processing time needed to train the network. |
| Starting Page | 115 |
| Ending Page | 124 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| Volume Number | 10 |
| Alternate Webpage(s) | http://www.m-hikari.com/ces/ces2011/ces1-4-2011/mustafaCES1-4-2011.pdf |
| Alternate Webpage(s) | http://wseas.us/e-library/transactions/computers/2011/52-400.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
