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Performance evaluation of open-cycle solar regenerator using artificial neural network technique

ENERGY AND BUILDINGS 2011年第2-3期43卷 454-457页

作者： Aly, Ayman A. Zeidan, El-Shafei B. Hamed, Ahmed M. Taif Univ Dept Mech Engn Fac Engn Al Hawyah Saudi Arabia

Theoretical investigation on the performance of lithium chloride (LiCl) absorption cooling system using an artificial neural network (ANN) model is presented. Tabulated data from the literature are used to construct the ANN model. Solar collector desiccant/regenerator is applied to re-concentrate the working solution. Using the proposed model, the effect of system design parameters;namely regenerator length, and air flow rate on the performance of the system is demonstrated. The variation of the thermo-physical parameters along the regenerator length is highlighted. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Solar energy Open-cycle Regeneration Lithium chloride Absorption Liquid desiccant Neural network back-propagation algorithm

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Design of an aperture-coupled microstrip antenna using a hybrid neural network

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IET MICROWAVES ANTENNAS & propagation 2012年第4期6卷 470-474页

作者： Bose, T. Gupta, N. Sikkim Manipal Inst Technol Dept Elect & Commun Rangpo 737312 East Sikkim India Birla Inst Technol Dept Elect & Commun Engn Ranchi 835215 Bihar India

In this study, an artificial neural network (ANN) model using hybrid neural network is proposed for the design of aperture-coupled microstrip antennas (ACMSAs). The new hybrid model is developed by combining radial basis function (RBF) and back-propagation algorithm (BPA). The performances evaluation of the hybrid model reveals superiority over the conventional BPA and RBF models in terms of error and time. The results obtained by the proposed model are compared with the simulation results obtained from the IE3D software package and also with the experimental results obtained from the fabricated ACMSA. The results show good agreement.

关键词： Electrical engineering computing radial basis function networks aperture antennas BPA Neural computing techniques electrical engineering computing backpropagation radial basis function hybrid neural network back-propagation algorithm Single antennas IE3D software package ACMSA artificial neural network aperture-coupled microstrip antenna design microstrip antennas ANN model RBF

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A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

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APPLIED INTELLIGENCE 2015年第4期43卷 855-873页

作者： Han, XiaoHong Xiong, Xiaoyan Duan, Fu Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan 030024 Peoples R China

This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the law of gravity and mass interactions. It has been proven that the GSA has good ability to search for the global optimum, but it suffers from the premature convergence due to the rapid reduction of diversity. This work introduces a cat chaotic mapping into the steps of population initialization and iterative stage of the original GSA, which forms a new algorithm called CCMGSA. Then the CCMGSA is employed to optimize BP neural networks, which forms a combination method called CCMGSA-BP and we use it for image segmentation. To verify the efficiency of this method, the visual and performance experiments are done. The visual results using our proposed method are compared with those using other segmentation methods including an improved k-means clustering algorithm (I-K-means), a hybrid region merging method (H-Region-merging), and manual segmentation. The comparison results show that the proposed method can get good segmentation results on grayscale images with specific characteristics. And we compare the performance of our proposed method with those of IGSA-BP, CLPSO-BP and RGA-BP for image segmentation. The results indicate that the CCMGSA-BP shows better performance in terms of the convergence rate and avoidance of local minima.

关键词： Image segmentation Gravitational search algorithm Cat chaotic mapping Neural network Optimization algorithm back-propagation algorithm

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Fault diagnosis on bottle filling plant using genetic-based neural network

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ADVANCES IN ENGINEERING SOFTWARE 2011年第12期42卷 1051-1058页

作者： Demetgul, M. Unal, M. Tansel, I. N. Yazicioglu, O. Marmara Univ Dept Mechatron Engn Fac Technol Istanbul Turkey Marmara Univ Dept Elect Comp Educ Tech Educ Fac Istanbul Turkey Florida Int Univ Dept Mech Engn Miami FL 33174 USA ITICU Dept Ind Engn Istanbul Turkey

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Neural network Genetic algorithm Bottle filling plant Pneumatic Fault diagnosis back-propagation algorithm

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Hindcasting of wave parameters using different soft computing methods

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APPLIED OCEAN RESEARCH 2008年第1期30卷 28-36页

作者： Mahjoobi, J. Etemad-Shahidi, A. Kazeminezhad, M. H. Iran Univ Sci & Technol Coll Civil Engn Tehran Iran

Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered from deep water location in Lake Ontario. Wind speed, wind direction, fetch length and wind duration were used as input variables, while significant wave height, peak spectral period and mean wave direction were the Output parameters. Different topologies of ANNs were considered to predict the wave parameters and the relative importance of input parameters were determined. Finally, the results of ANNs-based models, FIS- and ANFIS-based models were compared. Results indicated that error statistics of soft computing models were similar, while ANFIS models were marginally more accurate than FIS and ANNs models. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Wave hindcasting Artificial neural networks back-propagation algorithm Fuzzy inference system Adaptive-network Fuzzy clustering

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Performance prediction of a shower cooling tower using wavelet neural network

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APPLIED THERMAL ENGINEERING 2016年第0期108卷 475-485页

作者： Qi, Xiaoni Liu, Yongqi Guo, Qianjian Yu, Shanshan Yu, Jie Shandong Univ Technol Coll Traff & Vehicle Engn Zhangzhou Rd 12 Zibo 255049 Peoples R China Shandong Univ Technol Coll Mech Engn Zhangzhou Rd 12 Zibo 255049 Peoples R China

This paper develops an application of a wavelet neural network (WNN) based model to predict the performance of a shower cooling tower (SCT). In order to gather data to train and test the proposed WNN model, an experimental shower cooling tower was operated at some steady state conditions. Some of the experimental data are utilized for training. The model was used to predict various performance parameters of the system, namely the temperature difference of the outlet and inlet water stream and the dry bulb temperature, relative humidity of the outlet air stream, the water evaporative ratio and the cooling efficiency of the system. The performances of the WNN predictions were tested using experimental data not employed in the training process. The predictions yielded good agreement with the experimental values with correlation coefficients in the range of 0.9906-0.9986, mean relative errors in the range of 1.39-2.28% and very low root mean square errors. The results show that the WNN technique can be used effectively for predicting the performance characteristics of SCT under various operating conditions, then providing the theoretical basis on the research of heat and mass transfer inside SCT under any conditions, which is important for design and running control of the SCT system. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wavelet neural network back-propagation algorithm Shower cooling tower

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Outdoor Path Loss Predictions Based on Extreme Learning Machine

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期99卷 441-460页

作者： Popoola, Segun I. Misra, Sanjay Atayero, Aderemi A. Covenant Univ Dept Elect & Informat Engn Ota Nigeria Atilim Univ Dept Comp Engn Ankara Turkey

In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network back-propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura-Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively;and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively;and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast tr

关键词： Extreme Learning Machine Path loss prediction back-propagation algorithm Feed-forward neural network Radio network planning

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Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第1期61卷 1-10页

作者： El-Nagar, Ahmad M. El-Bardini, Mohammad Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia 32852 Egypt

Objective: This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. Methods and materials: The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm. Results: Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC. Conclusion: The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Hardware-in-the-loop back-propagation algorithm Interval type-2 fuzzy neural network Anesthesia Analgesia Muscle relaxation

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Biologically Fe²⁺ oxidizing fluidized bed reactor performance and controlling of Fe³⁺ recycle during heap bioleaching:: an artificial neural network-based model

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BIOPROCESS AND BIOSYSTEMS ENGINEERING 2008年第2期31卷 111-117页

作者： Ozkaya, Bestamin Sahinkaya, Erkan Nurmi, Pauliina Kaksonen, Anna H. Puhakka, Jaakko A. Tampere Univ Technol Inst Environm Engn & Biotechnol FIN-33101 Tampere Finland

The performance of a biological Fe2+ oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220 days at 37 degrees C under different operational conditions. A method is proposed for modeling Fe3+ production in FBR and thereby managing the regeneration of Fe3+ for heap leaching application, based on an artificial neural network-back-propagation algorithm. Depending on output value, relevant control strategies and actions are activated, and Fe3+ production in FBR was considered as a critical output parameter. The modeling of effluent Fe3+ concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations.

关键词： Fe3+ production precipitate neural network back-propagation algorithm FBR

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Combined Relay Selection Enabled by Supervised Machine Learning

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第4期70卷 3938-3943页

作者： Dang, Shuping Tang, Jiashen Li, Jun Wen, Miaowen Abdullah, Salwani Li, Chengzhong Guangxi Huanan Commun Co Ltd R&D Ctr Nanning 530007 Peoples R China King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Bangi 43600 Selangor Malaysia Guangzhou Univ Sch Elect & Commun Engn Ctr Intelligent Commun Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China

Combined relay selection only requires two relays to forward signals transmitted on multiple subcarriers, but the optimal outage performance is almost surely achievable in the high signal-to-noise ratio (SNR) region. However, because combined relay selection involves the generation of the full set of two-relay combinations, the selection complexity of combined relay selection is much higher than that of per-subcarrier relay selection when the number of relays goes large. This drawback restricts the implementation of combined relay selection in dense networks. To overcome this drawback, we propose to enable combined relay selection by supervised machine learning (ML). Because the training procedure is off-line, the proposed implementation scheme can considerably reduce the selection complexity and the processing latency. We carry out extensive experiments on TensorFlow 2.1 over a graphics processing unit (GPU) aided computing cloud server to validate the effectiveness of the proposed scheme. The experimental results confirm that supervised ML can provide near-optimal performance with lower computing latency that well matches that provided by brute-force search and the optimal relay selection in a per-subcarrier manner.

关键词： Relays Training Indexes Signal to noise ratio Neurons Machine learning Graphics processing units Artificial neural network (ANN) back-propagation algorithm cooperative relaying multi-carrier relay selection supervised machine learning (ML)

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