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Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2015年第4期62卷 1849-1856页

作者： Kim, Dong Yeong Yoo, Kwae Hwan Na, Man Gyun Chosun Univ Dept Nucl Engn Kwangju 501579 South Korea

It is very important for plant operators to be informed of the departure from nucleate boiling ratio (DNBR) to prevent the fuel cladding from melting and a boiling crisis in a nuclear reactor. The reactor core monitoring and protection systems require a minimum DNBR value to monitor reactor coolant conditions. In this study, in order to estimate the minimum DNBR value, a cascaded fuzzy neural network (CFNN) method was used. The CFNN model can be used to estimate the minimum DNBR value through the process of adding fuzzy neural networks (FNNs) repeatedly. The proposed DNBR estimation algorithm was verified by applying the nuclear and thermal data acquired from many numerical simulations of the optimized power reactor 1000 (OPR1000). The CFNN model was compared to previously developed models and was found to be superior to them. Therefore, this model can be used to effectively monitor and predict the minimum DNBR in the reactor core.

关键词： back-propagation algorithm cascaded fuzzy neural network (CFNN) departure from nucleate boiling ratio (DNBR) fuzzy neural network (FNN) fuzzy support vector regression (FSVR)

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The use of artificial neural network for an optical phenol biosensing based on tyrosinase entrapped in chitosan film

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SENSOR LETTERS 2006年第3期4卷 235-240页

作者： Abdullah, Jaafar Ahmad, Musa Heng, Lee Yook Karuppiah, Nadarajah Sidek, Hamidah Arip, Mohamad Nasir Mat Univ Kebangsaan Malaysia Sch Chem Sci & Food Technol Fac Sci & Technol Bangi 43600 SElangor DE Malaysia SIRIM Berhad Environm Bioproc Technol Ctr Shah Alam 40911 Selangor DE Malaysia

The use of artificial neural network (ANN) for signal processing in an optical phenol biosensing based on entrapped tyrosinase in chitosan film is presented. A multilayer feed-forward ANN with one hidden layer was trained via a back-propagation algorithm to adapt an input-output signal of an optical phenol biosensor. The results showed that the use of ANN technique was very effective in extending the limited dynamic response of the biosensor from 0.24-6.59 mg/L to 0.24-47.06 mg/L of phenol concentration. A network with 14 neurons in the hidden layer was able in predicting the biosensor response with an average error of 0.60 mg/L for detecting of unknown phenol concentration.

关键词： artificial neural network back-propagation algorithm biosensor tyrosinase phenol

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Deep Computation Model for Unsupervised Feature Learning on Big Data

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2016年第1期9卷 161-171页

作者： Zhang, Qingchen Yang, Laurence T. Chen, Zhikui Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China St Francis Xavier Univ Dept Math Stat & Comp Sci Antigonish NS B2G 1C0 Canada

Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.

关键词： Big data deep learning model tensor auto-encoder back-propagation algorithm feature learning

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Performance prediction of a solar thermal energy system using artificial neural networks

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APPLIED THERMAL ENGINEERING 2014年第1期73卷 1348-1359页

作者： Yaici, Wahiba Entchev, Evgueniy Nat Resources Canada CanmetENERGY Res Ctr Renewables & Integrated Energy Syst Lab Ottawa ON K1A 1M1 Canada

This paper describes in details an application of artificial neural networks (ANNs) to predict the performance of a solar thermal energy system (STES) used for domestic hot water and space heating application. Experiments were conducted on the STES under a broad range of operating conditions during different seasons and Canadian weather conditions in Ottawa, over the period of March 2011 through December 2012 to assess the system performance. These experimental data were utilised for training, validating and testing the proposed ANN model. The model was applied to predict various performance parameters of the system, namely the preheat tank stratification temperatures, the heat input from the solar collectors to the heat exchanger, the heat input to the auxiliary propane-fired tank, and the derived solar fractions. The back-propagation learning algorithm with two different variants, the Levenberg-Marguardt (LM) and scaled conjugate gradient (SCG) algorithms were used in the network. It was found that the optimal algorithm and topology were the LM and the configuration with 10 inputs, 20 hidden and 8 output neurons/outputs, respectively. The preheat tank temperature and solar fraction predictions agreed very well with the experimental values using the testing data sets. The ANNs predicted the preheat water tank stratification temperatures and the solar fractions of the STES within less that +/- 3% and +/- 10% errors, respectively. The results confirmed the effectiveness of this method and provided very good accuracy even when the input data are distorted with different levels of noise. Moreover, the results of this study demonstrate that the ANN approach can provide high accuracy and reliability for predicting the performance of complex energy systems such as the one under investigation. Finally, this method can also be exploited as an effective tool to develop applications for predictive performance monitoring system, condition monitoring, fault detection and diagno

关键词： Solar thermal energy system Thermal performance prediction Artificial neural networks back-propagation algorithm Numerical simulation

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Frequency-domain waveform inversion using an l₁-norm objective function

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EXPLORATION GEOPHYSICS 2009年第2期40卷 227-232页

作者： Pyun, Sukjoon Son, Woohyun Shin, Changsoo Seoul Natl Univ Res Inst Energy & Resources Seoul 151742 South Korea Seoul Natl Univ Dept Energy Syst Engn Seoul 151742 South Korea

In general, seismic waveform inversion adopts an objective function based on the l(2)-norm. However, waveform inversion using the l(2)-norm produces distorted results because the l(2)-norm is sensitive to statistically invalid data such as outliers. As an alternative, there have been several studies applying l(1)-norm-based objective functions to waveform inversion. Although waveform inversion based on the l(1)-norm is known to produce robust inversion results against specific outliers in the time domain, its effectiveness and characteristics are yet to be studied in the frequency domain. The present study proposes an algorithm for l(1)-norm-based waveform inversion in the frequency domain. The proposed algorithm employs a structure identical to those used in conventional frequency-domain waveform inversion algorithms that exploit the back-propagation technique, but displays robustness against outliers, which has been confirmed based on inversion of the synthetic Marmousi model. The characteristics and advantages of the l(1)-norm were analysed by comparing it with the l(2)-norm. In addition, inversion was performed on data containing outliers to examine the robustness against outliers. The effectiveness of removing outliers was verified by using the l(1)-norm to calculate the residual wave field and its spectrum for the data containing outliers.

关键词： back-propagation algorithm l(1)-norm waveform inversion

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A neural network learning algorithm of chemical process modeling based on the extended Kalman filter

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NEUROCOMPUTING 2007年第4-6期70卷 625-632页

作者： Yang, Huizhong Li, Jiang Ding, Feng So Yangtze Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China

The extended Kalman filtering (EKF) algorithm instead of the error back-propagation (BP) algorithm is used to train artificial neural networks (ANNs) for chemical process modeling. The basic idea is, by modifying the EKF gain, to prevent overfitting or filtering divergence phenomenon caused by outliers in the training samples. The EKF-based ANNs training method proposed is also applied to estimate the conversion rate in the polyacrylonitrile production process. Numerical simulations show that the modified EKF algorithm is superior to the BP algorithm in resisting noise and outliers, as well as generalization. (c) 2006 Elsevier B.V. All rights reserved.

关键词： back-propagation algorithm extended Kalman filter polyacrylonitrile production outlier

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Rapid quantitative elemental analysis using artificial neural network for case study of Isfahan Miniature Neutron Source Reactor

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JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY 2022年第11期331卷 4479-4487页

作者： Asgari, Afrouz Hosseini, Seyed Abolfazl Sharif Univ Technol Dept Energy Engn Tehran 863911365 Iran Nucl Sci & Technol Res Inst POB 81465-1589 Esfahan Iran

In this study, new method for NAA purposes at 30 kW Isfahan MNSR is suggested. An algorithm based on ANN is proposed to quantitatively predict the unknown elements with no need standard sample. A three-layer feed-forward ANN with back-propagation algorithm has been used to determine concentration of selenium and fluorine in Multiple Sclerosis patients and healthy people blood samples. Predicted concentration of elements show good agreement between new method and experiment results. The correlation coefficient between the experimentally determined and predicted values are 0.99104 and 0.99364, respectively. This method is a rapid and precise approach for elemental analysis.

关键词： Neutron activation analysis (NAA) Miniature neutron source reactor (MNSR) Artificial neural network (ANN) back-propagation algorithm Multiple sclerosis (MS)

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Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes

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JOURNAL OF MEMBRANE SCIENCE 2003年第1-2期220卷 155-164页

作者： Chakraborty, M Bhattacharya, C Dutta, S SV Reg Coll Engn & Technol Dept Chem Engn Surat 395007 India Jadavpur Univ Dept Chem Engn Kolkata 700032 W Bengal India

An artificial neural network (ANN) model of emulsion liquid membrane (ELM) process is proposed in the present study which is able to predict solute concentration in feed during extraction operation and ultimate % extraction at different initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time. Because of the complexity in generalization of the phenomenon of ELM process by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the back-propagation algorithm (BPA) for evaluating the connection strengths representing the correlations between inputs (initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time) and outputs (solute concentration in feed during extraction operation and % extraction). The network employed in the present study uses five input nodes corresponding to the operating variables and two output nodes corresponding to the measurement of the performance of the network (solute concentration in feed during extraction and % extraction). Batch experiments are performed for separation of nickel(II) from aqueous sulphate solution of initial concentration in the 200-100 mg/l ranges. The network employed in the present study uses two hidden layers of optimum number of nodes being thirty and twenty. A leaning rate of 0.3 and momentum factor of 0.4 is used. The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be well within +/-10%. (C) 2003 Elsevier B.V. All rights reserved.

关键词： emulsion liquid membrane artificial neural network back-propagation algorithm simulation solute concentration in feed phase

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Robust interval regression analysis using neural networks

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FUZZY SETS AND SYSTEMS 1998年第3期97卷 337-347页

作者： Huang, L Zhang, BL Huang, Q S China Univ Technol Inst Radio & Automat Guangzhou 510641 Peoples R China

As a very important tool for dealing with both crisp data and fuzzy data, fuzzy regression analysis based on interval regression analysis has become an active area of research. Some neural network related methods for nonlinear interval regression analysis have been proposed on the assumption that given training data are totally "good" data. The performance of these methods will significantly worsen when the training data are spoiled by outliers. In this paper, we introduce the concepts of polarity and quality of the training data, on the basis of which we propose two robust learning algorithms for determining a robust nonlinear interval regression model, which makes a feature of a new cost function for reflecting not only the polarity of the training data but also the estimated knowledge about the quality of the training data. The two robust algorithms are derived in a manner similar to the back-propagation (BP) algorithm. Simulation results show that our robust algorithms outperform the existing methods remarkably in two aspects when outliers are present: (1)They are robust against outliers;(2) Their rates of convergence are improved to some extent. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： interval regression fuzzy regression neural networks robust learning algorithms back-propagation algorithm

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Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks

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ROAD MATERIALS AND PAVEMENT DESIGN 2021年第7期22卷 1521-1542页

作者： Gupta, Tanvi Sachdeva, S. N. Natl Inst Technol Dept Civil Engn Kurukshetra Haryana India

In this paper, an attempt has been made to apply and compare the prediction capability of two variants of Artificial Neural Networks: feed-forward neural network (FFNN) and the radial basis function network (RBFN) for modelling the flexural and compressive strengths of jarosite mixed cement concrete for pavements. The compressive strength and the flexural strength are dependent upon a total of eight inputs. Their values are experimentally determined from the specimens containing 0%, 10%, 15%, 20% and 25% of the jarosite as a partial replacement to cement in M40 concrete mix. These specimens had undergone a curing for 3, 7, 28, 90, 180 and 365 days providing the inputs-outputs experimental data for the 90 observations. The simulation experiments showed that the performance of FFNN is found to be better than that of the RBFN model as the former model provided lesser values of the performance indicators such as Mean Average Error and Mean Squared Error. Further, the FFNN model required a lesser number of parameters to be tuned during the training as compared to the RBFN model.

关键词： jarosite radial basis function network back-propagation algorithm compressive and flexural strengths feed-forward neural network

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