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B-Jump: Roller length, sequent depth, and relative energy loss using Artificial Neural Networks

JOURNAL OF HYDRAULIC RESEARCH 2007年第4期45卷 529-537页

作者： Yazdandoost, F. Y. Bateni, S. A. Fazeli, M. KN Toosi Univ Technol Dept Civil Engn Tehran Iran MIT Dept Civil & Environm Engn Cambridge MA 02139 USA Sharif Univ Technol Dept Civil Engn Tehran Iran

The phenomenon of the hydraulic jump is so complex that despite considerable laboratory and prototype studies, estimation of its main characteristics in a generalized and accurate form is still difficult. The Artificial Neural Network (ANN) approach aims at limiting the needs for costly and timeconsuming experiments. In this study, two ANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis function using orthogonal least-squares algorithm (RBF/OLS), were used to predict the roller length, sequent depth, and the relative energy loss of the 13-jump. Based on a pre-specified range of jump parameters, the input vectors include: upstream bed slope (tan theta), inflow depth h 1, approach velocity V, and elevation of jump toe from the datum plane z(1), generated from the experimental data of Hager (J. Hydraul. Res., IAHR, 26(5), 539-558, 1988) and Kawagoshi and Hager (J. Hydraul. Res., IAHR, 28(4), 461-480, 1990). Once the network is trained to an acceptable level of accuracy, it produces an outputofjump rollerlength, sequentdepth, and relative energy loss for any inputvector. The predicted values agreewell with measurements. Sensitivity analysis was performed to investigate the importance of each input neuron. Finally a matrix of weights was specified for use at any given location.

关键词： hydraulic jump neural networks back propagation algorithm orthogonal least square algorithm roller length sequent depth energy loss

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Effect of temperature on short term load forecasting using an integrated ANN

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ELECTRIC POWER SYSTEMS RESEARCH 2004年第1期72卷 95-101页

作者： Satish, B Swarup, KS Srinivas, S Rao, AH Vellore Inst Technol Dept Elect & Elect Engn Vellore 632014 Tamil Nadu India Indian Inst Technol Dept Elect Engn Madras 600036 Tamil Nadu India Vellore Inst Technol Dept Math Vellore 632014 Tamil Nadu India

An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24 h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach. (C) 2004 Elsevier B.V. All rights reserved.

关键词： load forecasting artificial neural networks back propagation algorithm

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Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model

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IETE JOURNAL OF RESEARCH 2023年第5期69卷 3038-3051页

作者： El Hamidi, K. Mjahed, M. El Kari, A. Ayad, H. El Gmili, N. Cadi Ayyad Univ Lab Elect Syst Energy Efficiency & Telecommun BP 549Av Abdelkarim Elkhattabi Gueliz 4000 Marrakesh Morocco Royal Sch Aeronut Math & Syst Dept Marrakech 40000 Morocco Univ Hassan II Casablanca Condensed Matter Phys & Renewable Energy Lab Mohammedia BP 146 Mohammadia 28806 Morocco

This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn't require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control.

关键词： back propagation algorithm MIMO system MLP neural network NARMA-L2 quadcopter

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Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2005年第4期29卷 945-952页

作者： Kara, S Kemaloglu, S Güven, AE Erciyes Univ Dept Elect Engn TR-38039 Kayseri Turkey Erciyes Univ Dept Biomed Devices Technol TR-38039 Kayseri Turkey Erciyes Univ Civil Aviat Sch Dept Elect Elect TR-38039 Kayseri Turkey

This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97-100% specifity and correct classification is calculated to be 98-99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： femoral artery occlusion Welch autoregressive artificial neural network back propagation algorithm

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Image-based random rotation for preserving the data in data mining process

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第4期18卷 3893-3902页

作者： Sunil, Nadella Narsimha, G. Aditya Coll Engn & Technol Dept Comp Sci & Engn Surampalem Andhra Pradesh India JNTUH Univ Coll Engn Dept Comp Sci & Engn Sultanpur Telungana India

The privacy and security of big data have become a major concern in recent years, necessitating privacy-preserving data mining strategies to preserve the balance between data value and privacy. The application of data mining techniques to the web is known as web mining. The majority of consumers seek complete anonymity when using web apps and engaging in online activities, which raises privacy problems. Condensation, randomness, tree structure, and other traditional approaches are employed to maintain privacy. Existing techniques have limitations in that they are unable to balance data usefulness and may have privacy and scalability issues. Privacy-preserving tools such as encryption and machine learning techniques, among others, can be used to protect and classify the data stream. To overcome this, here, the data are transformed into image, and then, the random transformation is performed using enhanced particle swarm optimization. Here, the optimization is performed to identify the optimal random rotation of the data for both protection and better classification. The classification is performed through back propagation algorithm, and the perturbed data are tested against independent component analysis attack. The classification accuracy, computation time, and error rate of the classifier are measured, and it is compared with the existing method. The comparison result shows the achieved result of proposed method. This proposed system is done with the help of MATLAB 2021a.

关键词： Data mining Privacy protection Random rotation perturbation back propagation algorithm Particle swarm optimization

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Modelling of steam fired double effect vapour absorption chiller using neural network

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ENERGY CONVERSION AND MANAGEMENT 2006年第15-16期47卷 2202-2210页

作者： Manohar, H. J. Saravanan, R. Renganarayanan, S. Anna Univ Inst Energy Studies Dept Mech Engn Madras 600025 Tamil Nadu India

In this paper, steady state modelling of a double effect absorption chiller using steam as heat input is presented and discussed. The modelling is based on the artificial neural network (ANN) technique with 6-6-9-1 configuration. The neural network is a fully connected feed forward configuration using the back propagation learning algorithm. The model will predict the chiller performance based on the chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures and steam pressure. The network was trained with one year of experimental data and predicts the performance within +/- 1.2% of the actual values. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： absorption chiller artificial neural network back propagation algorithm coefficient of performance

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An approach to measure the densities of solids using an artificial neural network

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INSTRUMENTATION SCIENCE & TECHNOLOGY 2007年第2期35卷 189-199页

作者： Neelamegam, P. Rajendran, A. Nehru Mem Coll Autonomous PG & Res Dept Appl Phys Tiruchirappalli Tamil Nadu India Deemed Univ SASTRA Dept Elect & Instrumentat Engn Thanjavur Tamil Nadu India

A dedicated microcontroller based density measurement system is developed to measure densities of solids. A data acquisition system is designed and fabricated using a PIC16F877 microcontroller. To measure the weight and temperature of the sample, strain gauge and thermocouple sensors are used. A three layer neural network is used to train the data for atomic number, temperature, and density of sample using a back propagation algorithm. After training the neural network, it is used to compute the density at various temperatures.

关键词： density measurement temperature measurement microcontroller neural network back propagation algorithm

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EMSGD: An Improved Learning algorithm of Neural Networks With Imbalanced Data

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IEEE ACCESS 2020年 8卷 64086-64098页

作者： Qian Ya-Guan Ma Jun Zhang Xi-Min Pan Jun Zhou Wu-Jie Wu Shu-Hui Yun Ben-Sheng Lei Jing-Sheng Zhejiang Univ Sci & Technol Sch Sci Hangzhou 310023 Peoples R China Zhejiang Univ Sci & Technol Sch Big Data Sci Hangzhou 310023 Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China

In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed. The experimental results show that in the case of imbalanced data, the training error of neural network converges slowly and the generalization ability is poor. Our theoretical analysis shows that in the process of training, the gradient descent direction of the weights is dominated by the major-classes, which accounts for the slow convergence of the training error. Based on these results, we propose the Equilibration Mini-batch Stochastic Gradient Descent (EMSGD) method to ensure the equilibrium of the data in the mini-batch. The advantage of this technique is that it makes full use of the existing random sampling step of MSGD without increasing the computational complexity. In addition, by over-sampling of minor-classes in the mini-batch, duplicated instances would be greatly reduced, thus preventing the model from overfitting. The experimental results show that under the condition of the imbalanced training data, EMSGD can make the neural network training error converge rapidly.

关键词： Data imbalance back propagation algorithm stochastic gradient descent

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Hybrid Image Compression-Encryption Scheme Based on Multilayer Stacked Autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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Application of Legendre Neural Network for solving ordinary differential equations

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APPLIED SOFT COMPUTING 2016年 43卷 347-356页

作者： Mall, Susmita Chakraverty, S. Natl Inst Technol Dept Math Rourkela 769008 Odisha India

In this paper, a new method based on single layer Legendre Neural Network (LeNN) model has been developed to solve initial and boundary value problems. In the proposed approach a Legendre polynomial based Functional Link Artificial Neural Network (FLANN) is developed. Nonlinear singular initial value problem (IVP), boundary value problem (BVP) and system of coupled ordinary differential equations are solved by the proposed approach to show the reliability of the method. The hidden layer is eliminated by expanding the input pattern using Legendre polynomials. Error back propagation algorithm is used for updating the network parameters (weights). Results obtained are compared with the existing methods and are found to be in good agreement. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Differential equations Feed forward neural network model back propagation algorithm Functional Link Artificial Neural Network Legendre polynomial

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