Research Article
Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction
Table 1
Standard parameter set for training.
| Parameter | Value |
| Transfer function of the hidden neurons | Tan sigmoid | Transfer function of the output neurons | Log sigmoid | Training function | Trainscg | Maximal fail | 1 | Encoding | Real (decimal) | Chromosome length | 71 | Population size | 30 | Weight initialization routine | Rand | Initial range | ā1 ~ 1 | Fitness function | Mean square error | Selection operation | Roulette whe5el | Crossover | BLX ā 0.5 | Mutation | Non-uniform | Elitist | 2 | Stopping criterion | 100 iterations |
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