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Multi-threaded learning control mechanism for neural networks

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 87卷 16-34页

作者： Polap, Dawid Wozniak, Marcin Wei, Wei Damasevicius, Robertas Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Kaunas Univ Technol Software Engn Dept Studentu 50 Kaunas Lithuania

Neural networks are applicable in many solutions for classification, prediction, control, etc. The variety of purposes is growing but with each new application the expectations are higher. We want neural networks to be more precise independently of the input data. Efficiency of the processing in a large manner depends on the training algorithm. Basically this procedure is based on the random selection of weights in which neurons connections are burdened. During training process we implement a method which involves modification of the weights to minimize the response error of the entire structure. Training continues until the minimum error value is reached however in general the smaller it is, the time of weight modification is longer. Another problem is that training with the same set of data can cause different training times depending on the initial weight selection. To overcome arising problems we need a method that will boost the procedure and support final precision. In this article, we propose the use of multi-threading mechanism to minimize training time by rejecting unnecessary weights selection. In the mechanism we use a multi-core solution to select the best weights between all parallel trained networks. Proposed solution was tested for three types of neural networks (classic, sparking and convolutional) using sample classification problems. The results have shown positive aspects of the proposed idea: shorter training time and better efficiency in various tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Multi-threading Neural networks back-propagation algorithm

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Non-iterative estimation of heat transfer coefficients using artificial neural network models

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2005年第3-4期48卷 665-679页

作者： Sablani, SS Kacimov, A Perret, J Mujumdar, AS Campo, A Sultan Qaboos Univ Dept Food Sci & Nutr Muscat Oman Sultan Qaboos Univ Dept Soils Water & Agr Engn Muscat Oman Natl Univ Singapore Dept Mech & Prod Engn Singapore Singapore Univ Vermont Dept Mech Engn Burlington VT USA

The Inverse Heat Conduction Problem (IHCP) dealing with the estimation of the heat transfer coefficient for a solid/fluid assembly from the knowledge of inside temperature was accomplished using an artificial neural network (ANN). Two cases were considered: (a) a cube with constant thermophysical properties and (b) a semi-infinite plate with temperature dependent thermal conductivity resulting in linear and nonlinear problem, respectively. The Direct Heat Conduction Problems (DHCP) of transient heat conduction in a cube and in a semi-infinite plate with a convective boundary condition were solved. The dimensionless temperature-time history at a known location was then correlated with the corresponding dimensionless heat transfer coefficient/Biot number using appropriate ANN models. Two different models were developed for each case i.e. for a cube and a semi-infinite plate. In the first one, the ANN model was trained to predict Biot number from the slope of the dimensionless temperature ratio versus Fourier number. In the second, an ANN model was developed to predict the dimensionless heat transfer coefficient from non-dimensional temperature. In addition, the training data sets were transformed using a trigonometric function to improve the prediction performance of the ANN model. The developed models may offer significant advantages when dealing with repetitive estimation of heat transfer coefficient. The proposed approach was tested for transient experiments. A 'parameter estimation' approach was used to obtain Biot number from experimental data. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： inverse heat conduction problem (IHCP) back-propagation algorithm cube semi-infinite plate non-linear problem sensitivity analysis uncertainty analysis

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Artificial neural networks in predicting of the gas molecular diffusion coefficient

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年 200卷 407-418页

作者： Wang, Xiuqing Daryapour, Mahboobeh Shahrabadi, Abbas Pirasteh, Saied Razavirad, Fatemeh Shaoxing Univ Sch Mech & Elect Engn Inst Artificial Intelligence Shaoxing 312000 Peoples R China Islamic Azad Univ Mahshahr Branch Dept Chem Engn Mahshahr Iran Res Inst Petr Ind RIPI Petr Engn Dept Tehran Iran Aarhus Univ Struct Dynam & Geotech DK-8000 Aarhus Denmark Aalborg Univ Dept Mat & Prod Aalborg Denmark Hans Jensen Lubricat Co A S Res & Dept Hadsund Denmark

The diffusion coefficient is one of the most important parameters for designing two-phase operations between liquid and gas phases in refineries and petrochemical industries, as well as for the gas injection process in oil fields to enhance oil production. Accurate knowledge of this parameter is essential for the prediction of the dissolution rate of the gas phase into the liquid phase. Ideally, this parameter should be obtained experimentally. Given that setting up laboratory equipment and conducting experiments can be costly and time-consuming, mathematical modeling is used as an alternative. In some cases, this data is not either available or reliable, which poses a challenge for designs. Hence, empirically derived correlations are used to predict molecular diffusion coefficients. However, the success of empirical models depends mainly on the range of data at which they were originally developed. Empirical models are not comprehensive for applying to the other data. Recent studies demonstrated that the alternative approach to modeling complex processes and identifying the effective parameters is the artificial neural network (ANN), a suitable prediction method. This study presents a new model developed to predict the molecular diffusion coefficient of methane in crude oil. The model is developed using 172 data points collected from recent literature. Out of the total laboratory data, 90% (155 data points) were used for training the desired neural network, while 10% (17 data points) were reserved for testing and evaluating the performance of the network. The multi-layer perceptron (MLP) neural network architecture with back -propagation (BP) training algorithm was used successfully for the prediction of diffusion coefficients of methane in crude oil. The developed model is compared with the empirical data, which shows the developed model predicts the methane molecular diffusion coefficient in crude oil with an average absolute error of 4.18%.

关键词： Artificial neural networks Molecular diffusion coefficient Multi-layer perceptron back-propagation algorithm

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Performance analysis of HMM- and ANN-based spectrum vacancy predictor behaviour for cognitive radios

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2012年第4期11卷 206-213页

作者： Karia, Deepak C. Lande, B. K. Daruwala, R. D. Univ Mumbai SPIT Dept Elect & Telecommun Bombay 400058 Maharashtra India Univ Mumbai SAKEC Dept Elect Bombay 400088 Maharashtra India Univ Mumbai VJTI Dept Elect Bombay 400019 Maharashtra India

Cognitive Radios (CRs) are devices, which should be cognizant about the Spectrum Holes upon which the idea of a CR resides, which relates to the sensing and channel management function of the device. CR, therefore, must employ channel prediction techniques so as to decide the usage of channel and also prevents interference with the primary users (or incumbent users). In this paper, we use HMM to predict the channel usage patterns and to determine the channel occupancy states. We make use of BWA to train the parameters of the HMM model, Viterbi algorithm to estimate the hidden state of the channel and BWFA to predict the next state of the sequence. In addition to this, we compare the performance of the HMM-based model with that of a neural network-based predictor, which employs a time-delay feed forward network and which uses back propagation algorithm for training.

关键词： CRs cognitive radios channel prediction HMM hidden Markov models Baum-Welch algorithm Viterbi algorithm real-time data distributed data neural network back-propagation algorithm

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COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC algorithm WITH ADAPTIVE RELAXATION

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2008年第6期17卷 1089-1108页

作者： El Emam, Nameer N. Shaheed, Rasheed Abdul Philadelphia Univ Dept Comp Sci Amman Jordan

A method based on neural network with back-propagation algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

关键词： Neural network back-propagation algorithm genetic algorithm adaptive mesh finite element method

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Learning Petri Network and its application to nonlinear system control

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第6期28卷 781-789页

作者： Hirasawa, K Ohbayashi, M Sakai, S Hu, JL Kyushu Univ Dept Elect & Elect Syst Engn Fukuoka 812 Japan Kyushu Univ Dept Energy Convers Engn Fukuoka 812 Japan

According to recent knowledge of brain science, it is suggested that there exists functions distribution, which means that specific parts exist in the brain for realizing specific functions. This paper introduces a new brain-like model called Learning Petri Network (LPN) that has the capability of functions distribution and learning, The idea is to use Petri net to realize the functions distribution and to incorporate the learning and representing ability of neural network into the Petri net. The obtained LPN can be used in the same way as a neural network to model and control dynamic systems, while it is distinctive to a neural network in that it has the capability of functions distribution, An application of the LPN to nonlinear crane control systems is discussed. It is shown via numerical simulations that the proposed LPN controller has superior performance to the commonly-used neural network one.

关键词： back-propagation algorithm control neural network Petri net universal learning network

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Application of temporal difference learning rules in short-term traffic flow prediction

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EXPERT SYSTEMS 2015年第1期32卷 49-64页

作者： Abdi, Javad Moshiri, Behzad Islamic Azad Univ Dept Elect & Comp Engn NazarAbad Ctr Alborz Iran Univ Tehran Control & Intelligent Proc Ctr Excellence Sch Elect & Comp Engn Tehran Iran

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short-term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi-layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back-propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi-resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.

关键词： artificial neural networks back-propagation algorithm forecasting temporal difference learning traffic flows

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Quantum-Inspired Stacked Auto-encoder-Based Deep Neural Network algorithm (Q-DNN)

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 6929-6943页

作者： Patel, Om Prakash Tiwari, Aruna Bagade, Vinay Indian Inst Technol Indore Dept Comp Sci & Engn Simrol Indore India

In this paper, a quantum-inspired stacked auto-encoder-based deep neural network (Q-DNN) learning algorithm is proposed. The proposed Q-DNN uses stacked auto-encoder to form a deep neural network. This quantum computing concept has been used to optimize the learning parameters of the algorithm. To form a deep neural network, the selection of learning algorithms and its parameters is an important criterion for achieving better performance. However, learning capability depends upon the proper selection of learning parameters;improper selection may cause local minima or local maxima problem. To avoid improper selection of learning algorithm parameters, the quantum computing concept has been used, which is characterized by the representation of population dynamic, and evaluation function. It uses qubit for probabilistic representation which gives a large subspace for exploration to find an optimal value of the learning parameters. This concept has been utilized in the formation of stacked auto-encoder-based deep neural network by optimizing learning parameters of the learning algorithm. The proposed Q-DNN is trained and tested on various benchmark datasets such as the BUPA Liver Disorder, Ionosphere, PIMA Indians Diabetes, and the MNIST dataset on three different neural network architectures. Firstly, the deep neural network architecture is formed with one hidden layer, then deep neural network formed with two hidden layers, and finally deep neural network formed with three hidden layers. The proposed Q-DNN achieves promising results in terms of classification accuracy, sensitivity, and specificity in comparison with other approaches.

关键词： Deep neural network Stacked auto-encoder Quantum gates back-propagation algorithm Classification

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Multilayer neural networks and Bayes decision theory

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NEURAL NETWORKS 1998年第2期11卷 209-213页

作者： Funahashi, K Univ Aizu Ctr Math Sci Fukushima 965 Japan

There are many applications of multilayer neural networks to pattern classification problems in the engineering field. Recently, it has been shown that Bayes a posteriori probability can be estimated by feedforward neural networks through computer simulation. In this paper, Bayes decision theory is combined with the approximation theory an three-layer neural networks, and the two-category n-dimensional Gaussian classification problem is studied. First, we prove theoretically that three-layer neural networks with at least 2n hidden units have the capability of approximating the a posteriori probability in the two-category classification problem with arbitrary accuracy. Second, we prove that the input-output function of neural networks with at least 2n hidden units tends to the a posteriori probability as back-propagation learning proceeds ideally. These results provide a theoretical basis for the study of pattern classification by computer simulation. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： multilayer neural network Bayes decision theory back-propagation algorithm Bayes discriminant function Gaussian probability density A posteriori probability

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Development of a predictive system for car fuel consumption using an artificial neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4967-4971页

作者： Wu, Jian-Da Liu, Jun-Ching Natl Changhua Univ Educ Grad Inst Vehicle Engn Changhua 500 Taiwan

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuel consumption Artificial neural network back-propagation algorithm

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