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Neuro-genetic approach on logistic model based software reliability prediction

EXPERT SYSTEMS WITH APPLICATIONS 2015年第10期42卷 4709-4718页

作者： Roy, Pratik Mahapatra, G. S. Dey, K. N. Univ Calcutta Dept Comp Sci & Engn Kolkata 700009 India Natl Inst Technol Puducherry Dept Math Karaikal 609605 India

In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network Genetic algorithm back-propagation algorithm Logistic growth curve model Software reliability Prediction

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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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Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

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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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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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Feedforward maximum power point tracking of PV systems using fuzzy controller

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2002年第3期38卷 969-981页

作者： Veerachary, M Senjyu, T Uezato, K Univ Ryukyus Fac Engn Dept Elect & Elect Engn Okinawa 9030213 Japan

A feedforward maximum power (MP) point tracking scheme is developed for the interleaved dual boost (IDB) converter fed photovoltaic (PV) system using fuzzy controller. The tracking algorithm changes the duty ratio of the converter such that the solar cell array (SCA) voltage equals the voltage corresponding to the MP point at that solar insolation. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage. The reference voltage for the feedforward loop, corresponding to the MP point, is obtained by an off-line trained neural network. Experimental data is used for off-line training of the neural network, which employs back-propagation algorithm. The proposed fuzzy feedforward peak power tracking effectiveness is demonstrated through the simulation and experimental results, and are compared with the conventional proportional plus integral (PI) controller based system. Finally, a comparative study of interleaved boost and conventional boost converter for the PV applications is given and their suitability is discussed.

关键词： backpropagation feedforward fuzzy control neurocontrollers photovoltaic power systems solar cell arrays tracking PV systems back-propagation algorithm duty ratio error signal feedforward loop feedforward maximum power point tracking fuzzy controller fuzz photovoltaic power systems PILLING FEEDFORWARD stacking factor Fuzzy control maximum power point trackers tracking back propagation Neurocontrollers FUZZY CONTROLLER ERROR SIGNAL Solar cell arrays voltage reference

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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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Performance evaluation of chip breaker utilizing neural network

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2009年第2期209卷 647-656页

作者： Kim, Hong-Gyoo Sim, Jae-Hyung Kweon, Hyeog-Jun Pusan Natl Univ Sch Mech Engn Pusan 609735 South Korea Kyungnam Coll Informat & Technol Subdiv Mech & Automot Ind Pusan 617701 South Korea Sorabol Coll Dept Ship Bldg Technol Gyeongju 780711 South Korea

The continuous chip generated during turning operation deteriorates the workpiece precision and causes safety hazards for the operator. Appropriate control of the chip shape becomes a very important task for maintaining reliable machining process. In particular, effective chip control is necessary for a CNC machine or automatic production system because any failure in chip control can cause the lowering in productivity and the worsening in operation due to frequent stop. Therefore, a grooved chip breaker has been widely used for obtaining reliable discontinuous chips. in general, in order to develop a new cutting insert with a chip breaker, extensive time, research, and expense are required because several processes such as forming, sintering, grinding, and coating of products as well as different evaluation tests are necessary. In this study, the performance of commercial chip breakers was evaluated using a neural network that was trained through a back propagation algorithm. Important form elements (depth of cut, land, breadth, and radius) that directly influenced the chip formation were chosen among commercial chip breakers, and were used as input values of the neural network. As a result, the performance evaluation method has been developed and applied to commercial tools, which resulted in excellent performance. if the training data in the neural network is collected with greater consideration given to the effect of cutting conditions and the performance of chip breakers, it can be used for the design of chip breakers in the future. (c) 2008 Elsevier B.V. All rights reserved.

关键词： back-propagation algorithm Chip breaker Chip breaking Chip control Chip flow Curling Neural network

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