Control of Overfitting in Hierarchical Neural Networks with DFP Formula

prevalence of atopic dermatitis in children with type 1 diabetes mellitus in southeastern of iran (kerman province): a case-control study

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Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)

Dropout: a simple way to prevent neural networks from overfitting

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly d...

Overfitting and Neural Networks: Conjugate Gradient and Backpropagation

Methods for controlling the bias/variance tradeoff typically assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (MLP) neural networks, global parameters such as the training time (e.g. based on validation tests), network size, or the amount of weight decay are commonly used to control the bias/variance tradeoff. However, the degree of overfitting can vary...