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作者： Horak, Bohumil Snasel, Vaclav Department of Measurement and Control VšB - Technical University of Ostrava Czech Republic Faculty of Electrical Engineering and Computer Science Czech Republic Czech Republic Faculty of Electrical Engineering and Computer Science VšB - Technical University of Ostrava Department of Computer Science Czech Republic

ISBN: (纸本)008045108X

We have developed a method of representing robotic soccer game. This representation is used for controlling robots playing soccer. Our approach to robot soccer is to view it as a local interaction game, which reduces the need for centralized control. in this paper we describe both hardware and software implementation that is used by robots. Virtual grid is described. The virtual grid is much more efficient than representation of game using natural coordinates of robots. This representation can be efficiently used for a description of game strategies, which can be directly used for a faster control of robots. We also show how a can be learned and illustrate our approach using the latent semantic analysis. Strategy learning is important both for developing successful players and also for discovering strategies of the opponent team. Copyright © 2005 IFAC.

关键词： Game theory

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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User-Oriented Methodology and Techniques of Decision Analysis and Support 1

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丛书名： Lecture Notes in Economics and Mathematical Systems

1000年

作者： Jaap Wessels Andrzej Piotr Wierzbicki

ISBN: (数字)9783662225875

ISBN: (纸本)9783540563822

This book presents selected papers from an international workshop devoted tothe theory, techniques and tools of decision analysis and support. Major trends in the development of this field are stressed, such as the tendency to place the final user of a decision support system in the center of attention, or an emerging connection between tools and software environments for modeling and for decision support. The volume is acontinuation of the reports on earlier meetings which were published in the same series.

关键词： Operations Research/Decision Theory IT in Business

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Sine Cosine Algorithm for Simple recurrent neural network Tuning for Stock Market Prediction 30

Sine Cosine Algorithm for Simple recurrent neural network Tu...

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30th Telecommunications Forum, TELFOR 2022

作者： Jovanovic, Luka Milutinovic, Nemanja Gajevic, Masa Krstovic, Jelena Rashid, Tarik A. Petrovic, Aleksandar Technical Faculty Singidunum University Belgrade Serbia University of Kurdistan Computer Science and Engineering Department Hewler44017 Iraq Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Deep artificial neural networks have recently gained popularity in the time series forecasting literature. Recurrent neural networks' higher suitability for this type of problem is the reason why this type of network has been chosen over other deep neural network approaches. Due to the number of parameters used the simplicity of these networks is considerable. This characteristic makes deep recurrent neural networks highly suitable for the problems of forecasting. Unfortunately, finding recurrent neural architecture for each specific task is NP-hard, therefore employment of metaheuristics is appropriate. Accordingly, the research proposed in this paper tackles tuning simple recurrent neural networks by sine cosine algorithm for stock market prediction. The proposed method's performance was compared with other metaheuristics and validated against the Nikkei stock exchange. © 2022 IEEE.

关键词： Recurrent neural networks

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Distributed computing and Artificial Intelligence 1

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丛书名： Advances in Intelligent and Soft computing

1000年

作者： Andre Ponce Leon F. de Carvalho Sara Rodríguez-González Juan F. Paz Santana Juan M. Corchado Rodríguez

ISBN: (数字)9783642148835

ISBN: (纸本)9783642148828

The International Symposium on Distributed computing and Artificial Intel- gence (DCAI´10) is an annual forum that brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application to provide efficient solutions to real problems. This symposium is organized by the Biomedicine, Intelligent System and Edu- tional Technology Research Group (http://bisite. usal. es/) of the University of - lamanca. The present edition has been held at the Polytechnic University of - lencia, from 7 to 10 September 2010, within the Congreso Español de Informática (CEDI 2010). Technology transfer in this field is still a challenge, with a large gap between academic research and industrial products. This edition of DCAI aims at contributing to reduce this gap, with a stimulating and productive forum where these communities can work towards future cooperation with social and econo- cal benefits. This conference is the forum in which to present application of in- vative techniques to complex problems. Artificial intelligence is changing our - ciety. Its application in distributed environments, such as internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to cite some, is continuously increasing, becoming an e- ment of high added value with social and economic potential, both industry, life quality and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in universities, companies.

关键词： Computational Intelligence Artificial Intelligence

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Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Induction motor broken rotor bar detection using vibration analysis — A case study

Induction motor broken rotor bar detection using vibration a...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Ž. Kanović D. Matić Z. Jeličić M. Rapaić B. Jakovljević M. Kapetina Computing and Control Department Faculty of Technical Science University of Novi Sad Novi Sad Serbia

Early fault diagnosis and condition monitoring can reduce the consequential damage and breakdown maintenance, prolong the machine life and increase the performance of industrial systems. This paper describes a real fault detection problem of a high-power (3.2 MW) induction motor driving pumps in a heating plant. Steady-state vibration signals were collected and some characteristic low- and high- frequency features were observed, resulting in broken rotor bar diagnosis. The obtained results were verified by disassembling the motor, which proved that this particular technique can be successfully applied in induction motor fault detection.

关键词： Induction motors Rotors Vibrations Synchronous motors Fault detection Educational institutions Frequency-domain analysis

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Sufficient stability conditions of fractional systems with perturbed differentiation orders

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IFAC-PapersOnLine 2017年第1期50卷 14557-14562页

作者： Malti, Rachid Rapai, Milan Bordeaux University IMS UMR 5218 CNRS 351 cours de la Libration Talence cedex33405 France University of Novi Sad Faculty of Technical Sciences Computing and Control Department Trg D. Obradovica 6 Novi Sad Serbia

In this paper sufficient stability conditions are established for fractional systems with perturbed differentiation orders. It is an extension of the recently published paper Rapaic and Malti [2016] which allows henceforth increasing the highest differentiation order. The maximum allowable variation on all differentiation orders is computed so that the stability (respectively instability) is preserved when differentiation orders are perturbed away from the commensurate ones. The maximum allowable variations are compared in both cases: when the highest order is allowed to be increased and when it is not. The established conditions allow concluding on the stability of incommensurate fractional systems on the basis of Matignon's theorem and the additional sufficient condition. © 2017

关键词： System stability

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State of the art in nonlinear dynamical system identification using Artificial Neural Networks

State of the art in nonlinear dynamical system identificatio...

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8th Seminar on Neural Network Applications in Electrical Engineering, Neurel-2006

作者： Todorovic, Nenad Klán, Petr Czech Technical University in Prague Faculty of Mechanical Engineering Department of Instrumentation and Control Engineering Institute of Computer Science Prague Czech Republic

ISBN: (纸本)1424404339

This paper covers the state of the art in nonlinear dynamical system identification using Artificial Neural Networks (ANN). The main approaches in the last two decades are presented in unified framework. ANN have unique characteristics, which enable them to model nonlinear dynamical systems. The main problems with the choice of ANN model structure are considered and commonly used identification schemes are proposed. A procedure for derivation of parameter estimation law using Lyapunov synthesis approach, which guarantees stability and convergence of the overall identification scheme, is presented. © 2006 IEEE.

关键词： Neural networks

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Application of interpretability method based on FN-v8 in defect detection of photovoltaic panels 3

Application of interpretability method based on FN-v8 in def...

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3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Zhou, Li Sun, Xueliang Liu, Yiming Faculty of Electrical and Control Engineering Liaoning Technical University Huludao China Information Science Department of Beijing University of Technology Beijing China

ISBN: (纸本)9781510671423

Aiming at the problems of poor interpretability of traditional neural network and bloated traditional model in photovoltaic panel defect detection. Firstly, an FN-v8 model was proposed for detecting various types of defects in photovoltaic panels. The FN-v8 model reduces redundant computing and memory access, while also reducing the GFLOPs of the model. Then, Grad-CAM was used to further demonstrate the stronger interpretability of the new model through passive interpretation. Compared with other traditional models with relatively bulky structures, the proposed new model has smaller parameter quantities and stronger interpretability. Experiments were conducted on devices with the same performance and the same dataset of photovoltaic panel defects. The results showed that the proposed new model has a smaller volume, faster calculation speed, and higher accuracy than the original model. The GFLOPs of the new model have decreased by 18.25% compared to the original model, significantly reducing the computational complexity, and the visualization of the thermodynamic diagram is more accurate, further indicating an improvement in interpretability. Moreover, the new model has better detection accuracy for photovoltaic panel detection, with an average detection accuracy( mAP@0.5 )Improved by 2.1% compared to the original model. © 2023 SPIE.

关键词： Defects

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