2,754 Hits in 3.8 sec

Supervised learning of photovoltaic power plant output prediction models

L. Prokop, S. Mišák, V. Snášel, J. Platos, P. Krömer
2013 Neural Network World  
The method is compared to artificial neural networks and support vector regression that were also used to build predictors in order to analyse a time-series like data describing the production of the PVPP  ...  The models of the PVPP are created using different supervised machine learning methods in order to forecast the short-term output of the power plant and compare the accuracy of the prediction.  ...  The fuzzy rule is in this research enhanced by the ability to process data as an ordered (time-like) series of records and it is used to estimate the power output of a PVPP.  ... 
doi:10.14311/nnw.2013.23.020 fatcat:q66ohjdi2fek7ee7sd25eagdci

Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

Chi-Chung Chen, Yi-Ting Liu
2018 Computational Intelligence and Neuroscience  
Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated  ...  the accuracy of fuzzy system design.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2018/9485478 pmid:29568311 pmcid:PMC5820572 fatcat:2tf6prxenzevxlm4y4fox22ryu

Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution

Xinyu Wang, Kegui Chen, Xueping Tan
2018 Mathematical Problems in Engineering  
information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting.  ...  Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method  ...  The genetic algorithm is used to optimize the assignment of fuzzy information controlling points, and a coding algorithm of multidimensional information controlling points is applied to make genetic-fuzzy  ... 
doi:10.1155/2018/3868923 fatcat:xaoxzv7ewragfhlbki3srtj55i

Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm

Xiuge Tan, Wei Wang
2021 Complexity  
Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are  ...  background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm  ...  basic methods principles of chaotic genetic algorithm and fuzzy decision algorithm; Section 3 constructs a prediction model for the economic chaotic time series based on the algorithm of chaotic genetics  ... 
doi:10.1155/2021/5517502 fatcat:ix3qqyos7bendfmtefzgc7qwva

Pattern Recognition and Its Application in Solar Radiation Forecasting [chapter]

Mahmoud Ghofrani, Rasool Azimi, Mastaneh Youshi
2019 Pattern Recognition [Working Title]  
By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy.  ...  The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting.  ...  Finally, another NAR network is applied as a global predictor for the solar radiation time-series data.  ... 
doi:10.5772/intechopen.83503 fatcat:pynv6e3snndrflaycylutamjga

Intelligent Hybrid model to Enhance Time Series Models for Predicting Network Traffic

Theyazn H.H. Aldhyani, Melfi Alrasheed, Ahmed Abdullah Alqarni, Mohammed Y. Alzahrani, Alwi M. Bamhdi
2020 IEEE Access  
Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series.  ...  The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models.  ...  The Long Short-Time Memory (LSTM) neural network algorithm is an RNN algorithm that uses in series time domain.  ... 
doi:10.1109/access.2020.3009169 fatcat:w4333r7anfcyzjlbg6555stbla

Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules [chapter]

Pavel Krömer, Tomáš Novosád, Václav Snášel, Vicente Vera, Beatriz Hernando, Laura García-Hernandez, Héctor Quintián, Emilio Corchado, Raquel Redondo, Javier Sedano, Alvaro E. García
2012 Lecture Notes in Computer Science  
This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures -evolutionary fuzzy rules and flexible neural trees -for the  ...  prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine.  ...  This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no.  ... 
doi:10.1007/978-3-642-32639-4_100 fatcat:2ykx3ycvxfc45fbitb7fd6jtpy

Heart Disease Prediction Methods

In recent times, heart diseases are considered one of the deadliest causes of mortality and morbidity among the population of the world.  ...  Predicting the probability of the occurrence of cardiovascular diseases has become one of the most important objectives of the medical analysis system.  ...  Also, accuracy can be increased by including various other related medical attributes. Instead of categorical data, continuous data can be used with various time series data mining algorithms.  ... 
doi:10.35940/ijitee.g1017.0597s20 fatcat:sr3obcpjfrejrjmywsczizhrba

Wind Speed Forecasting in China: A Review

Huiru Zhao
2015 Science Journal of Energy Engineering  
This paper can rich the current research in the field of wind speed forecasting.  ...  China's wind power has developed rapidly in the past few years, the large-scale penetration of which will bring big influence on power systems.  ...  [41] used SVM and genetic algorithm to forecast the wind speed. The genetic algorithm was employed to optimize the penalty factor C and kernel parameter σ 2 of support vector machines.  ... 
doi:10.11648/j.sjee.s.2015030401.13 fatcat:dpbqaw57o5c6pm7ozgnv7zwppe


Srikanta Patnaik, Srikanta Patnaik
2018 Journal of Intelligent & Fuzzy Systems  
They have used Genetic Algorithm to rank the newest methods of the AETS.  ...  identifying relevant features from sentences using Genetic Algorithms.  ... 
doi:10.3233/jifs-169562 fatcat:2qknpgafxjdaza2ppepzpwxreu

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Mojtaba Asoodeh, Parisa Bagheripour, Amin Gholami
2015 Acta Geophysica  
Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative  ...  Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances.  ...  Therefore, the use of hybrid genetic algorithm-pattern search tool instead of back-propagation algorithm in the structure of neural network will improve the accuracy of modeling and eliminate the probability  ... 
doi:10.1515/acgeo-2015-0003 fatcat:lcl4odrdvnbtrnppyuvaklaeku

Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Shabib Aftab, Munir Ahmad, Noureen Hameed, Muhammad Salman, Iftikhar Ali, Zahid Nawaz
2018 International Journal of Advanced Computer Science and Applications  
This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction.  ...  Rainfall prediction is one of the challenging tasks in weather forecasting.  ...  Time series data is collected over a specific period of time such as hourly, daily, weekly, monthly, quarterly or yearly [23] , [40] .  ... 
doi:10.14569/ijacsa.2018.090518 fatcat:lqf2acdfb5gr3l5isirwycqegy

Price Prediction of Stock Market- An Empirical Research

2020 International journal of recent technology and engineering  
Financial markets are one of the key points of any country economy.  ...  In this study, an extensive review of existing techniques dedicated to stock market forecasting is carried out.  ...  the Stock Exchange of Singapore (SES), Integrated approach using Genetic Algorithm (GA), fuzzy neural network.  ... 
doi:10.35940/ijrte.a2083.059120 fatcat:ujrb3qa4dvg5hbxu4in6kuz4eu

Technical Approach in Text Mining for Stock Market Prediction: A Systematic Review

Mohammad Rabiul Islam, Imad Fakhri Al-Shaikhli, Rizal Bin Mohd Nor, Vijayakumar Varadarajan
2018 Indonesian Journal of Electrical Engineering and Computer Science  
Due to research significance, this empirical research also highlights the limitation of different strategies and methods on exact aspects of theoretical framework for enhancing of performance.  ...  To find the meaningful information from the vast amount of electronic textual data become a humongous task for trading decision.  ...  ACKNOWLEDGEMENTS This research work was partially supported by International Islamic University Malaysia, FRGS14-127-0368 and ERGS13-018-0051 from Ministry of Higher Education of Malaysia.  ... 
doi:10.11591/ijeecs.v10.i2.pp770-777 fatcat:n5hf2opjczdfneo5lnhtg6eske

A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

Gyu M. Lee, Xuehong Gao
2021 Applied Sciences  
To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation  ...  Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11167428 fatcat:le4dfyxwobb5viecq52glvym7i
« Previous Showing results 1 — 15 out of 2,754 results