Optimizing hidden layer node number of BP network to estimate fetal weight

2007 ◽  
Author(s):  
Juan Su ◽  
Yuanwen Zou ◽  
Jiangli Lin ◽  
Tianfu Wang ◽  
Deyu Li ◽  
...  
Keyword(s):  
Fetal Weight ◽  
Bp Network ◽  
Node Number ◽  
Hidden Layer ◽  
Estimate Fetal Weight

scholarly journals BP Network for Predicting the TBM utilization

2021 ◽  
Author(s):  
Ningbo Li ◽  
Yan Zhu ◽  
Qingxia Xiao ◽  
Xiaobin Xu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Joint Spacing ◽  
Bp Network ◽  
Node Number ◽  
Model Based ◽  
Hidden Layer ◽  
Bpnn Model

Abstract This paper proposed the idea of combining genetic algorithm (GA) with BP (back propagation) neural network, and establishes the TBM tunneling utilization prediction model based on BPNN-GA. Based on the analysis of rock parameters affecting TBM utilization, the rock mass grade, uniaxial compressive strength UCS and joint spacing DPW are selected as the input parameters for TBM utilization prediction. The TBM utilization prediction model based on BPNN-GA is established. The node number and super parameters of hidden layer are determined by empirical formula. The prediction results of bpnn-ga model are combined with the traditional BPNN model The results show that, compared with the traditional BPNN model, BPNN has been improved under the optimization of genetic algorithm, the prediction accuracy on the test set is increased by about 8.95%, and the mean square error is reduced by about 60%. BPNN-GA model does not rely on specific data sets in prediction, showing good portability and generalization.

Integration Particle Swarm Algorithm and its Application in Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2133-2136
Author(s):  
Jun Pan ◽  
Xu Cao
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Bp Network ◽  
Node Number ◽  
The Neural Network ◽  
Swarm Algorithm ◽  
Hidden Layer

This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

scholarly journals Swedish intrauterine growth reference ranges for estimated fetal weight

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Lindström ◽  
Mårten Ageheim ◽  
Ove Axelsson ◽  
Laith Hussain-Alkhateeb ◽  
Alkistis Skalkidou ◽  
...  
Keyword(s):  
Fetal Growth ◽  
Postnatal Growth ◽  
Fetal Weight ◽  
Hierarchical Regression ◽  
Reference Ranges ◽  
Hierarchical Regression Model ◽  
Gestational Week ◽  
Prospective Longitudinal ◽  
Estimate Fetal Weight ◽  
Estimated Fetal Weight

AbstractFetal growth restriction is a strong risk factor for perinatal morbidity and mortality. Reliable standards are indispensable, both to assess fetal growth and to evaluate birthweight and early postnatal growth in infants born preterm. The aim of this study was to create updated Swedish reference ranges for estimated fetal weight (EFW) from gestational week 12–42. This prospective longitudinal multicentre study included 583 women without known conditions causing aberrant fetal growth. Each woman was assigned a randomly selected protocol of five ultrasound scans from gestational week 12 + 3 to 41 + 6. Hadlock’s 3rd formula was used to estimate fetal weight. A two-level hierarchical regression model was employed to calculate the expected median and variance, expressed in standard deviations and percentiles, for EFW. EFW was higher for males than females. The reference ranges were compared with the presently used Swedish, and international reference ranges. Our reference ranges had higher EFW than the presently used Swedish reference ranges from gestational week 33, and higher median, 2.5th and 97.5th percentiles from gestational week 24 compared with INTERGROWTH-21st. The new reference ranges can be used both for assessment of intrauterine fetal weight and growth, and early postnatal growth in children born preterm.

Failure Modes Model of MEMS Accelerometers Based on Neural Networks

2008 ◽  
Author(s):  
Yanping Bai ◽  
Ping An ◽  
Yilong Hao
Keyword(s):  
Neural Networks ◽  
Failure Modes ◽  
Late Phase ◽  
Primary Sources ◽  
Bp Network ◽  
Bp Networks ◽  
Mems Accelerometers ◽  
Perception Model ◽  
Hidden Layer ◽  
Training Success

Fabrication of a MEMS system involves design, testing, packaging and reliability related issues. However, reliability issues that are discovered at a late phase may cause major delays in the product development going together with high costs. In this paper we study the failure modes and Mechanisms of MEMS accelerometers products and present the classification modeling of failure modes based on neural networks. In ours MEMS accelerometers, there are six failure mechanisms that have been found to be the primary sources of failure nodes. We introduce nonlinear BP network with a hidden layer and linear perception to classify for MEMS accelerometers products. Classification results show that nonlinear BP network seem to be most appropriate to approach the problem of failure modes classification than linear perception. BP neural network is capable of learning the intrinsic relations of the patterns with which they were trained. For all experiments results, the training success of rate is 100% for both methods. BP networks obtained a high forecast success of rate of over 99.5%. The linear perception model obtained a success of rate of over 95.5%. We also analyze the technology stability of MEMS products.

The Use of a Software-Assisted Method to Estimate Fetal Weight at and Near Term Using Magnetic Resonance Imaging

2017 ◽  
Vol 41 (4) ◽  
pp. 307-313 ◽  
Author(s):  
Caroline Kadji ◽  
Maxime De Groof ◽  
Margaux F. Camus ◽  
Riccardo De Angelis ◽  
Stéphanie Fellas ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fetal Weight ◽  
Resonance Imaging ◽  
Near Term ◽  
Estimate Fetal Weight

LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell

2019 ◽  
Vol 12 (2) ◽  
pp. 274-291 ◽  
Author(s):  
Haoqiang Shi ◽  
Shaolin Hu ◽  
Jiaxu Zhang
Keyword(s):  
Prediction Accuracy ◽  
Random Noise ◽  
Single Point ◽  
Noise Pollution ◽  
Temperature Data ◽  
Bp Network ◽  
Content Type ◽  
Shell Temperature ◽  
Lstm Network ◽  
Hidden Layer

Purpose Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change. Design/methodology/approach In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope. Findings By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds. Practical implications The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope. Originality/value In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

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