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International Conference on Inventive Computation Technologies

作者： Heena Mhatre Varsha Bhosale Department of Information Technology Vidyalankar Institute of Technology Mumbai India

ISBN: (纸本)9781509012862

In the past years modern arithmetical methods for image investigation have led to a rebellion in many fields, from computer vision to scientific imaging. Though, some recently developed image processing techniques successfully oppressed by other sections have been infrequently, if ever, experimented on celestial observations. Here we present a new idea of super resolution of Astronomical objects using back propagation algorithm."Super-resolution "is efficient in improving the excellence of analysis of diffused sources formerly unobserved by the background noise, efficiently rising the depth of obtainable observations. Higher-resolution image out of a set of low resolution frames can be obtained through super-resolution. Super-resolution is viable only for point sources which have negligible dimensions, then for wideranging objects the knowledge about intensity vacillation at angular prevalence is irreversibly mislaid. Again obtaining super resolved image for extended sources(e.g. comets, meteoroids, etc) is a new challenge if the speed of the object is very high. Acquiring High resolution images of celestial objects from ground based telescopes is intricate and often requires computational post processing techniques to remove blur caused by atmospheric commotion. Even images obtained through satellite imaging are compressed and sent to earth. So there is need for Super resolution of those compressed or noisy images. So, here we simply implement Super-resolution for Astronomical objects using back propagation algorithm to overcome lost information and challenges for high speedy celestial objects. The purpose is to super resolve high speedy celestial objects whose analysis may in future help to prevent collisions of such celestial objects with earth and also avoid future solar system damage

关键词： Super resolution Astronomical observations back propagation algorithm Low resolution images Gradient descent function mean square error function Gaussian kernel Linear Kernel Blur Sigma factor sparse toeplitz matrix bicubic interpolation High resolution image

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Wind Power Prediction Based on back propagation algorithm with Numerical Weather Prediction 2019

Wind Power Prediction Based on Back Propagation Algorithm wi...

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Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Guo Peng Wang Cun College of Electronic Information Engineering Inner Mongolia University

In this paper, the back propagation (BP) neural network wind power prediction model and the BP neural network wind power model with numerical weather prediction are established. The research shows that the BP neural n... 详细信息

ISBN: (纸本)9781450376631

关键词： back propagation algorithm Wind power prediction Numerical weather prediction

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Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms

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Journal of Applied Research and Technology 2016年第3期14卷 206-214页

作者： Premalatha, Neelamegam Valan Arasu, Amirtham Department of Mechanical Engineering Kamaraj College of Engineering and Technology Virudhunagar Tamil Nadu 626001 India Department of Mechanical Engineering Thiagarajar College of Engineering Madurai Tamil Nadu 625015 India

Global solar radiation (GSR) is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R). Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE) which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India. © 2016 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico.

关键词： Artificial neural network back propagation algorithm Global solar radiation

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Design of PSS for multi-machine system using extreme learning machine algorithm

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MEASUREMENT 2025年 247卷

作者： Suman, M. Rao, M. Venu Gopala Veerendra, A. S. Mopidevi, Subbarao Marquez, Fausto Pedro Garcia Vignans Lara Inst Technol & Sci Dept Elect & Elect Engn Vadlamudi 522213 India Prasad V Potluri Siddhartha Inst Technol Dept Elect & Elect Engn Kanuru 520007 India Manipal Inst Technol Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal 576104 India Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India Univ Castilla La Mancha Ingn Res Grp Albacete Spain

A novel Extreme Learning Machine algorithm is used to train the neural network for Power System Stabilizer (PSS) to minimize low-frequency oscillations. The use of rapid-acting exciters, the interconnection of various power systems, and disturbances like faults and load changes all contribute to the generation of low-frequency oscillations. If sufficient damping is not provided, these oscillations generate and sustain, and eventually cause the power system to shut down entirely. The lead-lag power system stabilizer is a conventional device used but it is slow in operation and can apply to linear systems only. Artificial intelligence techniques like, fuzzy and neural networks are used to overcome the bottlenecks. The neural networks are trained using backpropagation and extreme learning algorithms. The operation of the designed power system stabilizers is verified on a 7-machine, 29-bus system, 4-machine, 11-bus system, and SMIB System. The proposed controller has been providing better damping performance compared to other controllers in terms of Integral Time Squared Error (ITSE) and Integral Squared Error (ISE). The proposed system is designed and validated through the R2023b MATLAB/Simulink Software environment.

关键词： Power System Stabilizer Power System Stability Fuzzy Controller back propagation algorithm Extreme Learning Machine algorithm

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Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

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INFORMATION SCIENCES 2023年第1期637卷

作者： Zheng, Kunming Zhang, Qiuju Peng, Li Zeng, Shuisheng Jiangnan Univ Sch Mech Engn 1800 Lihu Ave Wuxi 214122 Peoples R China Jiangsu Key Lab Adv Food Mfg Equipment & Technol 1800 Lihu Ave Wuxi 214122 Peoples R China Jiangnan Univ Sch Internet Things Engn 1800 Lihu Ave Wuxi 214122 Peoples R China Changzhou Gucoi Intelligent Equipment Technol Res Changzhou 213001 Peoples R China

This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.

关键词： Robot Memetic evolution algorithm Differential evolution algorithm back propagation algorithm Fuzzy neural network

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A work point count system coupled with back-propagation for solving double dummy bridge problem

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NEUROCOMPUTING 2015年 168卷 160-178页

作者： Amalraj, R. Dharmalingam, M. Bharathiyar Univ Coimbatore Sri Vasavi Coll Dept Math & Comp Sci Erode 638316 Tamil Nadu India Bharathiyar Univ Coimbatore Sri Vasavi Coll Nanda Arts & Sci Coll Erode 638316 Tamil Nadu India

The game 'contract bridge' is one of the most widely known card games comprising many fascinating aspects, such as bidding, playing and winning the trick including estimation of hand strength, the additional input data based on the human knowledge of the game to improve the quality of results. The game classified under a game of imperfect information is to be equally well-defined, since the decision made on any stage of the game is purely based on the decision that was made on the immediate preceding stage. The incompleteness of information, the real spirit of the card game in proceeding further deals of the game are taking into many forms especially during the distribution of cards for the next deal. One among the architectures of the Artificial Neural Network is considered by training on sample deals and used to estimate the number of tricks taken by one pair of bridge players is the key idea behind the Double Dummy Bridge Problem, implemented in this paper. The Cascade Correlation Neural Network architecture with supervised learning implemented to train data and hence to test it is coupled along with Work point count system. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial Neural Networks Cascade-Correlation Neural Network back propagation algorithm Sigmoid functions Contract bridge Double Dummy Bridge Problem Work point count system

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AN ANALYSIS OF PREMATURE SATURATION IN back-propagation LEARNING

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NEURAL NETWORKS 1993年第5期6卷 719-728页

作者： LEE, Y OH, SH KIM, MW Electronics and Telecommunications Research Institute Daejeon Korea

The back propagation (BP) algorithm is widely used for finding optimum weights of multilayer neural networks in many pattern recognition applications. However, the critical drawbacks of the algorithm are its slow learning speed and convergence to local minima. One of the major reasons for these drawbacks is the ''premature saturation '' which is a phenomenon that the error of the neural network stays significantly high constant for some period of time during learning. It is known to be caused by an inappropriate set of initial weights. In this paper, the probability of premature saturation at the beginning epoch of learning procedure in the BP algorithm has been derived in terms of the maximum value of initial weights, the number of nodes in each layer, and the maximum slope of the sigmoidal activation function;it has been verified by the Monte Carlo simulation. Using this result, the premature saturation can be avoided with proper initial weight settings.

关键词： PREMATURE SATURATION back propagation algorithm 1ST EPOCH MULTILAYER PERCEPTRON

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Short Term Electrical Load Forecasting Using back propagation Neural Networks

Short Term Electrical Load Forecasting Using Back Propagatio...

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North American Power Symposium (NAPS)

作者： Reddy, S. Surender Momoh, James A.

ISBN: (纸本)9781479959044

This paper presents a new approach for short term electrical load forecasting (STLF) using artificial neural networks (ANN), and examines the feasibility of various mathematical models for STLF. To make these mathematical models to yield satisfactory and acceptable results, various system models are formulated considering various combination of parameters like base load component, day of the week, load inertia, short term trends, auto-correlation, length of the past data, etc. Various modifications of back propagation algorithm (BPA) have been proposed, to explore the ideal combination that suit the forecasting need of large utilities like regional electricity grids. Further, the load dynamics are extensively studied to identify the parameters for system modeling.

关键词： Load forecasting load demand artificial neural networks back propagation algorithm

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Pre-processed back propagation Neural Networks for CDMA Interference Cancellation

Pre-processed Back propagation Neural Networks for CDMA Inte...

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IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN)

作者： Geevarghese, Biju Thomas, Jibu Anil, Muth Francis, Anish Ambedkar Univ Hyderabad Bihar India Toc H Inst Sci & Technol Dept ECE Arakkunnam India Amal Jyothi Coll Engn Dept ECE Kottayam Kerala India Kerala State Elect Board Trichur Kerala India

ISBN: (纸本)9781467350372;9781467350365;9781467350358

This paper attempts to show the effects of pre-processing in bit error rate of neural network interference cancellation in spread spectrum receivers. Multiple access interference (MAI) which is one of the primary issues in CDMA multiuser detection is combated by interference cancellers. Feed-Forward Neural Networks with back propagation is used for interference cancellers. The works shows that pre-processing can bring adequate improvement in bit error rate as well as complexity.

关键词： Code Division Multiple Access Neural network Bit Error Rate Pre-processing back propagation algorithm

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back propagation and Genetic algorithms for Control of the Network Element

Back Propagation and Genetic Algorithms for Control of the N...

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34th International Conference on Telecommunications and Signal Processing (TSP)

作者： Skorpil, Vladislav Kamba, Stanislav Brno Univ Technol Dept Telecommun Brno 61200 Czech Republic

The authors have for long been interested in the design of the network element for multimedia networks. The designed network element is not controlled sequentially, as is still usual in neural networks. In this contri... 详细信息

ISBN: (纸本)9781457714115

关键词： Neural network back propagation algorithm Genetic algorithm

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