Comparison of Neural Network Training Algorithms for the prediction of the patient's post-operative recovery area

An Artificial Neural Network(ANN) is a well known universal approximator to model smooth and continuous functions. ANNs operate in two stages: learning and generalization. Learning of a neural network is to approximate the behavior of the training data while generalization is the ability to predict well beyond the training data. In order to have a good learning and generalization ability , a good training algorithm is needed. Training a neural network can be treated as a nonlinear mathematical optimization problem and different algorithms can have quite different effects on the training result. As a result, training with different algorithms and repeating with multiple random initial weights can be helpful in getting a better solution to the neural network training problem. In addition to the popular basic back propagation training algorithm, many other algorithms are available. These include conjugate gradient descent, quasi-Newton, and Levenberg-Marquardt etc. This paper presents a novel comparison of different algorithms for the prediction of the patient’s postoperative recovery area.