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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


Papers
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Journal ArticleDOI
TL;DR: Numerical tests demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty and the performance of these two uncertainty quantification methods is assessed through reliability.
Abstract: This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

51 citations

Journal ArticleDOI
TL;DR: This review is aimed to both astronomers and computer scientists (who often know little about potentially interesting applications), and will focus their attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining.

51 citations

Journal ArticleDOI
TL;DR: The results show the effectiveness of the proposed approach in modelling the surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs) compared with other soft computing techniques.
Abstract: A study is presented to model surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs). The machining parameters, namely, the spindle speed, feed rate, depth of cut and the workpiece-tool vibration amplitude have been used as inputs to model the workpiece surface roughness. The number and the parameters of membership functions used in ANFIS along with the most suitable inputs are selected using GAs maximising the modelling accuracy. The ANFIS with GAs (GA-ANFIS) are trained with a subset of the experimental data. The trained GA-ANFIS are tested using the set of validation data. The procedure is illustrated using the experimental data of a CNC vertical machining centre in end-milling of 6061 aluminum. Results are compared with other soft computing techniques like genetic programming (GP) and artificial neural network (ANN). The results show the effectiveness of the proposed approach in modelling the surface roughness.

51 citations

Journal ArticleDOI
TL;DR: A case study of five meteorological stations located in Kurdistan province in the west of Iran shows soft computing models were superior to the empirical methods in modelling ET0, and the ANN was found to be better than the ANFIS and GEP.
Abstract: Evapotranspiration assessment is one of the most substantial issues in hydrology. The methods used in modelling reference evapotranspiration (ET0) consist of empirical equations or complex methods based on physical processes. In arid and semi-arid climates, determining the amount of evapotranspiration has a major role in the design of irrigation systems, irrigation network management, planning and management of water resources and water management issues in the agricultural sector. This paper presents a case study of five meteorological stations located in Kurdistan province in the west of Iran. The ability of three different soft computing methods, an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), were compared for modelling ET0 in this study. The FAO56 Penman−Monteith model was considered as a reference model and soft computing models were compared using the Priestley−Taylor, Hargreaves, Hargreaves−Samani, Makkink and Makkink−Hansen empirical methods, with respect to the determination co-efficient, the root mean square error, the mean absolute error and the Nash–Sutcliffe model efficiency co-efficient. Soft computing models were superior to the empirical methods in modelling ET0. Among the soft computing methods, the ANN was found to be better than the ANFIS and GEP.

51 citations

Journal ArticleDOI
TL;DR: The development of improved neural networks based short-term electric load forecasting models for the power system of the Greek Island of Crete are presented and the embedding of the new model capability in a modular forecasting system is presented.

51 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348