When the traditional BP neural network has a big size of neurons in its hidden layers,it can own a very strong ability in fitting practical complex objective functions,but simultaneously for the same reason,the over-f...When the traditional BP neural network has a big size of neurons in its hidden layers,it can own a very strong ability in fitting practical complex objective functions,but simultaneously for the same reason,the over-fitting problem is almost inevitable and will be more serious when there is only a very restricted size of training data.A new BP neural network optimisation method is given based on dynamical regularization(DRBP)in this paper.Differing from the traditional regularization method with an invariant prior assumption,this proposed method carries out weight decaying with adjusting regularization parameter dynamically according to the stability of the network during the whole training process.The results of experiments represented in this paper have shown that our method can antagonise the over-fitting problem effectively,reinforcing the generalisation ability of the model,and as an obvious result,the classification accuracy on the testing data is promoted.展开更多
The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships amon...The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.展开更多
文摘When the traditional BP neural network has a big size of neurons in its hidden layers,it can own a very strong ability in fitting practical complex objective functions,but simultaneously for the same reason,the over-fitting problem is almost inevitable and will be more serious when there is only a very restricted size of training data.A new BP neural network optimisation method is given based on dynamical regularization(DRBP)in this paper.Differing from the traditional regularization method with an invariant prior assumption,this proposed method carries out weight decaying with adjusting regularization parameter dynamically according to the stability of the network during the whole training process.The results of experiments represented in this paper have shown that our method can antagonise the over-fitting problem effectively,reinforcing the generalisation ability of the model,and as an obvious result,the classification accuracy on the testing data is promoted.
文摘The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.
