BOF Endpoint Prediction Based on Multi-Neural Network Model

2013 ◽  
Vol 441 ◽  
pp. 666-669 ◽  
Author(s):  
You Jun Yue ◽  
Ying Dong Yao ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Data ◽  
Neural Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Control Precision ◽  
Detection Technology ◽  
Multi Neural Network

In order to solve the problem that the small and middle converters unable to introduce the sublance detection technology to improve the control precision of endpoint because of the constraints of economy and technology, a method which combine the pedigree cluster and neural network is studied, the pedigree cluster divide the large data sets into several categories, the degree of similarity will be relatively high in each category after division, then train neural model for every category. Finally make predictions. Simulation results show that the multi-neural network model has better prediction results.

A Principal Components Analysis Self-Organizing Neural Network Model and Computational Experiment

2013 ◽  
Vol 756-759 ◽  
pp. 3330-3335
Author(s):  
Ji Fu Nong
Keyword(s):  
Neural Network ◽  
Principal Components Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Principal Components ◽  
Computational Experiment ◽  
Neural Model ◽  
Self Organizing Map ◽  
Components Analysis ◽  
Self Organizing

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

scholarly journals A convolutional neural network-based screening tool for X-ray serial crystallography

2018 ◽  
Vol 25 (3) ◽  
pp. 655-670 ◽  
Author(s):  
Tsung-Wei Ke ◽  
Aaron S. Brewster ◽  
Stella X. Yu ◽  
Daniela Ushizima ◽  
Chao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Data ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
X Ray ◽  
X Ray Crystallography ◽  
Automatic Image Processing

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Min Zheng ◽  
Dajun Du ◽  
Yihuan Li ◽  
Minrui Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Neural Model ◽  
State Of Charge ◽  
Battery Pack ◽  
Soc Estimation ◽  
Battery Packs

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

scholarly journals Neural Network Model for Design Constructability Assessment

2012 ◽  
Author(s):  
Rosli Mohamad Zin ◽  
Muhd. Zaimi Abd. Majid ◽  
Che Wan Fadhil Che Wan Putra ◽  
Abdul Hakim Mohammed
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Output Variable ◽  
Artificial Neural Network Ann ◽  
Project Data ◽  
Hidden Nodes

Kertas ini memaparkan satu kajian mengenai penilian kebolehbinaan reka bentuk menggunakan kaedah rangkaian neural tiruan (ANN). Model neural timbalbalik berbilang lapis yang dihasilkan mengandungi 12 pemboleh ubah input dan 1 pemboleh ubah output. Pemboleh ubah input terdiri dari tahap gunapakai faktor-faktor kebolehbinaan iaitu merupakan subfaktor prinsip-prinsip terpenting kebolehbinaan fasa reka bentuk manakala pemboleh ubah output adalah tahap kebolehbinaan reka bentuk. Pembangunan model dibuat melalui lima peringkat: mengenal pasti prinsip-prinsip kebolehbinaan fasa reka bentuk, menentukan darjah kepentingan prinsip-prinsip kebolehbinaan, menghasilkan satu kerangka untuk mengukur tahap guna pakai prinsip-prinsip kebolehbinaan dan tahap reka bentuk, mengumpul data-data projek terdahulu, dan mengguna pakai kaedah ANN untuk menilai kebolehbinaan reka bentuk. Setiap peringkat pembentukan model adalah diterangkan. Data-data projek terdahulu berkaitan pembinaan rasuk telah dikumpulkan dari kontraktor-kontraktor yang mempunyai pengalaman beberapa tahun dalam pembinaan bangunan. Sejumlah 78 set data telah digunakan untuk melatih dan menguji rangkaian neural. Penentuan bilangan optima bagi nod tersembunyi, aras tersembunyi, pemberat awalan bagi penghubung antara nod, dan bilangan iterasi latihan adalah berdasarkan cubaan dan ralat. Artikek rangkaian neural terbaik didapati mengandungi 12 nod input, 5 nod tersembunyi dan 1 nod output. Kata kunci: Rangkaian neural tiruan (ANN), kebolehbinaan reka bentuk, model neural timbalbalik berbilang lapis, faktor, faktor-faktor kebolehbinaan This paper presents an artificial neural network (ANN) technique of analysis for the assessment of design constructability. The multilayer back-propagation neural network model consists of 12 and 1 output variable. The input variables are the level of applications of constructability factors, which are sub-factors of the most important design phase constructability principles while the output variable is the level of design constructability. The development of the model goes through five main stages: identifying the design phase constructability principles, identifying the degree of importance of the constructability principles, formulating a framework for measuring the level of application of constructability principles and design constructability, collecting historical project data, and applying ANN to assess design constructability. Each stage of the model development is described. Historical project data sets related to beam construction have been collected from various constructors that have at least several years of experience in building construction. A total of 78 data sets were used to test and train the network. The determination of the optimum number of hidden nodes, hidden layers, initial weights of the links connecting the nodes, and the number of epochs for training the networks, are normally based on trial and error. The best architecture was found to consist of 12 input nodes, 5 hidden nodes, and 1 output node. Key words: Artificial neural network (ANN), design constructability, multilayer back-propagation neural network model, constructability factors

Abnormal State Identification for T Beam Based on Novelty Detection

2014 ◽  
Vol 898 ◽  
pp. 818-821
Author(s):  
De Yan Wang ◽  
Tao Wang ◽  
Li Liu ◽  
Yan Gao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Novelty Detection ◽  
Load Test ◽  
Sensor Data ◽  
Beam Model ◽  
Detection Technology ◽  
T Beam ◽  
Crack Position

The cracking state and abnormal positions are recognized during T beam model tests using the BP neural network based novelty detection technology. Neural network training sample data is generated by analyzing the static load test data, the neural network model based on novelty detection technology is established, the state of the T beam anomaly recognition and crack position recognition is accomplished. Stepwise partition method is used in crack position recognition, which includes narrowing the crack position as the first step, specifically analyzing the sensor data, and determination of the crack position. T beam neural network model is verified by the measured data. The results show that, the method can accurately identify state and effectively identify the location of the crack.

scholarly journals An autoencoder-based neural network model for selectional preference: evidence from pseudo-disambiguation and cloze tasks

2017 ◽  
Vol 8 (2) ◽  
pp. 93-125
Author(s):  
Aki-Juhani Kyröläinen ◽  
Juhani Luotolahti ◽  
Filip Ginter
Keyword(s):  
Neural Network ◽  
Semantic Similarity ◽  
Network Model ◽  
Neural Network Model ◽  
Semantic Information ◽  
Contextual Information ◽  
Neural Model ◽  
The Other ◽  
Excellent Performance

Intuitively, some predicates have a better fit with certain arguments than others. Usage-based models of language emphasize the importance of semantic similarity in shaping the structuring of constructions (form and meaning). In this study, we focus on modeling the semantics of transitive constructions in Finnish and present an autoencoder-based neural network model trained on semantic vectors based on Word2vec. This model builds on the distributional hypothesis according to which semantic information is primarily shaped by contextual information. Specifically, we focus on the realization of the object. The performance of the model is evaluated in two tasks: a pseudo-disambiguation and a cloze task. Additionally, we contrast the performance of the autoencoder with a previously implemented neural model. In general, the results show that our model achieves an excellent performance on these tasks in comparison to the other models. The results are discussed in terms of usage-based construction grammar.Kokkuvõte. Aki-Juhani Kyröläinen, M. Juhani Luotolahti ja Filip Ginter: Autokoodril põhinev närvivõrkude mudel valikulisel eelistamisel. Intuitiivselt tundub, et mõned argumendid sobivad teatud predikaatidega paremini kokku kui teised. Kasutuspõhised keelemudelid rõhutavad konstruktsioonide struktuuri (nii vormi kui tähenduse) kujunemisel tähendusliku sarnasuse olulisust. Selles uurimuses modelleerime soome keele transitiivsete konstruktsioonide semantikat ja esitame närvivõrkude mudeli ehk autokoodri. Mudel põhineb distributiivse semantika hüpoteesil, mille järgi kujuneb semantiline info peamiselt konteksti põhjal. Täpsemalt keskendume uurimuses objektile. Mudelit hindame nii valeühestamise kui ka lünkülesande abil. Kõrvutame autokoodri tulemusi varem välja töötatud neurovõrgumudelitega ja tõestame, et meie mudel töötab võrreldes teiste mudelitega väga hästi. Tulemused esitame kasutuspõhise konstruktsioonigrammatika kontekstis.Võtmesõnad: neurovõrk; autokooder; tähendusvektor; kasutuspõhine mudel; soome keel

scholarly journals ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

2020 ◽  
Vol 60 (5) ◽  
pp. 440-447
Author(s):  
. Rustam ◽  
Agus Yodi Gunawan ◽  
Made Tri Ari Penia Kresnowati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.

scholarly journals Outlet Temperature Prediction of Boiling Heat Transfer in Helical Coils through Artificial Neural Network

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 16
Author(s):  
Krisana Insom ◽  
Patcharin Kamsing ◽  
Thaweerath Phisannupawong ◽  
Peerapong Torteeka
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Network Model ◽  
Neural Network Model ◽  
Boiling Heat Transfer ◽  
Neural Model ◽  
Outlet Temperature ◽  
Ann Model ◽  
Testing Stage ◽  
Helical Coils

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

Sign in / Sign up

Export Citation Format

Share Document