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Analysis of Some Feedforward Artificial Neural Network Training algorithms for Developing Localization Framework in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2015年第4期82卷 2519-2536页

作者： Payal, Ashish Rai, C. S. Reddy, B. V. R. Guru Gobind Singh Indraprastha Univ Delhi India

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.

关键词： Localization back propagation algorithm Neural network Bayesian regularization Multi-layer perceptron Wireless sensor network

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Multilayer feed-forward neural network approach for optimal dispatch of UPFC embedded pool-bilateral electricity market

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第9期25卷 1923-1942页

作者： Geetha, Natarajan Renuga, Perumal Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi 626005 India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India

The significance and influence of bilateral component on optimal dispatch of mixed pool-bilateral electricity market is the major issue to be examined for trade-off between pool and pre-specified bilateral transactions. This paper proposes single part optimal dispatch model that dispatches pool in coordination with privately negotiated bilateral transactions while minimizing cost of generation, accounting power balance equality constraints and inequality constraints. The critically loaded transmission line is identified, and when Unified Power Flow Controller (UPFC) is placed in it, optimal dispatch solution gets modified because of incorporation of UPFC parameters in the developed formulation. The methodology of multilayer perceptron network is adopted in this work with inputs to the neural network as percentage of bilateral component and status of UPFC for online evaluation of generation cost and incremental cost. The network is trained with back propagation training algorithm, and for IEEE 30 and IEEE 118 bus systems, it is established that Levenberg-Marquardt algorithm outperforms other algorithms in terms of accuracy. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： optimal dispatch mixed pool-bilateral model UPFC back propagation algorithm artificial neural network transaction matrix

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Color image vehicular detection systems with and without fuzzy neural network: A comparison

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2003年第5期26卷 659-670页

作者： Lan, LW Kuo, AY Huang, YC Natl Chiao Tung Univ Inst Traff & Transportat Taipei 100 Taiwan

This paper develops a color image vehicular detection (CIVD) system in which background differencing technique is employed to detect whether a vehicle passes through the detecting points equally spaced out on a pseudo line detector. Two methods (interval search and regression) are tried to determine the optimal crisp threshold values to cope with various lighting conditions. To compare the detection performance with and without incorporating a fuzzy neural network (FNN), a three-layer FNNCIVD system is further designed with trapezoidal membership function and network parameters trained by back propagation algorithm. Under different environments (freeway and urban street) with various lighting conditions (daytime and nighttime), it is found that the detection success rates for interval-search CIVD and regression CIVD are about the same. However, both perform worse than the FNNCIVD system in which about 90% success rates are reported with seven detection points. Compared with the interval-search CIVD system, the FNNCIVD system can increase the success rates at a range of 14% to 22% on the freeway mainline and 18% to 26% on the urban street. It is also found that daytime detection performance is slightly better than nighttime detection. Possible reasons for missed detection and false detection are discussed.

关键词： color image vehicular detection (CIVD) fuzzy neural network (FNN) background differencing technique back propagation algorithm

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Neural networks analysis of thermal characteristics on plate-fin heat exchangers with limited experimental data

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APPLIED THERMAL ENGINEERING 2009年第11-12期29卷 2251-2256页

作者： Peng, Hao Ling, Xiang Nanjing Univ Technol Sch Mech & Power Engn Nanjing 210009 Jiangsu Peoples R China

drop and heat transfer characteristics in the plate-fin heat exchangers (PFHEs). First, the thermal performances of five different PFHEs were evaluated experimentally. The Colburn factor j and friction factor f to different type fins were obtained under various experimental conditions. Then, a feed-forward neural network based on back propagation algorithm was developed to model the thermal performance of the PFHEs. The ANNs was trained using the experimental data to predict j and f factors in PFHEs. Different network configurations were also examined for searching a better network for prediction. The predicted values were found to be in good agreement with the actual values from the experiments with mean squared errors (MSE) less than 1.5% for j factor and 1% for f factor, respectively. This demonstrated that the neural network presented can help the engineers and manufacturers predict the thermal characteristics of new type fins in PFHEs under various operating conditions. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Fin Artificial neural network back propagation algorithm Colburn factor Friction factor

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Thermal performance investigation of the miniature revolving heat pipes using artificial neural networks and genetic algorithms

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2020年第0期151卷 119394-000页

作者： Liang, Feng Gao, Jianmin Xu, Liang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Peoples R China Guangdong Xian Jiaotong Univ Acad Foshan 528300 Guangdong Peoples R China

The miniature revolving heat pipes (MRVHPs) are promising candidates for the cooling structure design of rotating machines, however, the relation between the thermal performance of MRVHPs and their thermo-physical properties is still absent. Therefore, MRVHPs are tested in various operational conditions firstly. And then, a power-law empirical correlation is developed based on experimental data, Ku, Ja, Pr, Bo and Fr are determined as main effective dimensionless parameters, phi while the structural parameters of MRVHPs D-i/R, L-e/L-c, L-e/L-eff and filling ratio yo are considered as well. However, the prediction accuracy is not satisfactory enough. Considering ANNs have been widely used in varied applications and demonstrated to be particularly credible in system modeling and identification, therefore, a BPNN model which parameterized by GA is developed for thermal performance prediction of the MRVHPs. The experimental data are divided into training data set and testing data set. Ja, Pr, Bo, Fr and phi are regarded as inputs, while Ku is output. The results show that the established GA-BPNN model could predict thermal performance of MRVHPs with a very good accuracy. Comparing with the predicted results of semi-empirical correlation, the square of correlation coefficient (R-2) is increased by 13.265%. Meanwhile, the evaluation method for the optimal filling ratio of the MRVHPs under specified working conditions is developed and an acceptable accuracy is obtained. (C) 2020 Published by Elsevier Ltd.

关键词： Miniature revolving heat pipes Thermal performance forecasting Artificial neural networks back propagation algorithm Genetic algorithm Optimal filling ratio estimation

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A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2003年第11-12期22卷 883-889页

作者： Dixit, US Chandra, S Indian Inst Technol Dept Mech Engn Gauhati 781039 India

Neural network models can be effectively used to predict any type of functional relationship. In this paper, a neural network model is used to predict roll force and roll torque in a cold flat rolling process, as a function of various process parameters. A strategy is developed to obtain a prescribed accuracy of prediction with a minimum number of data for training and testing. The effect of increasing the size of training and testing data set is also examined. After the prediction of most likely value, upper and lower bound estimates are also found with the help of the neural network. With these estimates, the predicted value can be represented as a fuzzy number for use in fuzzy-logic based systems.

关键词： cold flat rolling neural networks back propagation algorithm prediction of roll force and roll torque

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Study of a laboratory-scale froth flotation process using artificial neural networks

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MINERAL PROCESSING AND EXTRACTIVE METALLURGY REVIEW 2008年第2期29卷 130-142页

作者： Kalyani, V. K. Pallavika Chaudhuri, Sanjay Charan, T. Gouri Haldar, D. D. Kamal, K. P. Badhe, Y. P. Tambe, S. S. Kulkarni, B. D. IT Ctr Cent Min Res Inst Dhanbad 826001 Bihar India Cent Fuel Res Inst Dhanbad Bihar India Vinoba Bhave Univ Dept Chem Hazaribagh India NCL Div Chem Engn Pune Maharashtra India

A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

关键词： back propagation algorithm froth flotation laboratory-scale neural network

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Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network

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IET ELECTRIC POWER APPLICATIONS 2008年第1期2卷 71-76页

作者： Pan, C. Chen, W. Yun, Y. Chongqing Univ State Key Lab Safety & New Technol Power Transmis Chongqing 630044 Peoples R China

The main drawbacks of a back propagation algorithm of wavelet neural network (WNN) commonly used in fault diagnosis of power transformers are that the optimal procedure is easily stacked into the local minima and cases that strictly demand initial value. A fault diagnostic method is presented based on a real-encoded hybrid genetic algorithm evolving a WNN, which can be used to optimise the structure and the parameters of WNN instead of humans in the same training process. Through the process, compromise is satisfactorily made among network complexity, convergence and generalisation ability. A number of examples show that the method proposed has good classifying capability for single- and multiple-fault samples of power transformers as well as high fault diagnostic accuracy.

关键词： fault diagnostic method power transformers fault diagnosis backpropagation hybrid genetic algorithm neural nets wavelet neural network power engineering computing back propagation algorithm

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Neural network modelling of Abbott-Firestone roughness parameters in honing processes

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INTERNATIONAL JOURNAL OF SURFACE SCIENCE AND ENGINEERING 2017年第6期11卷 512-530页

作者： Sivatte-Adroer, Maurici Buj-Corral, Irene Llanas-Parra, Xavier Univ Politecn Cataluna Dept Mech Engn Sch Engn Vilanova I la Geltru EPSEVG Av Victor Balaguer 1 Barcelona 08800 Spain Univ Politecn Cataluna Dept Mech Engn Sch Engn Barcelona ETSEIB Av Diagonal 647 E-08028 Barcelona Spain Univ Politecn Cataluna Sch Engn Vilanova I la Geltru EPSEVG Automat Control Dept Av Victor Balaguer 1 Barcelona 08800 Spain

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.

关键词： surface roughness honing artificial neural networks ANN backpropagation algorithm Abbott-Firestone roughness parameters back propagation algorithm grain size density of abrasive linear speed tangential speed pressure

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A general inverse control model of a magneto-rheological damper based on neural network

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JOURNAL OF VIBRATION AND CONTROL 2022年第7-8期28卷 952-963页

作者： Yan, Yaya Dong, Longlei Han, Yi Li, Weishuo Xi An Jiao Tong Univ 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper's inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network's initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current's prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.

关键词： Magneto-rheological damper back propagation algorithm optimized genetic algorithm inverse model semi-active control

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