Search Results - subject:"neurofuzzy"

A new strategy for predicting short-term wind speed using soft computing models

Haque, Ashraf U.; Mandal, Paras; Kaye, Mary E.; Meng, Julian - In: Renewable and Sustainable Energy Reviews 16 (2012) 7, pp. 4563-4573

), a radial basis function neural network (RBFNN), and an adaptive neuro-fuzzy inference system (ANFIS). The forecasting …

Persistent link: https://www.econbiz.de/10010664557

Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

Rajkumar, R.K.; Ramachandaramurthy, V.K.; Yong, B.L.; … - In: Energy 36 (2011) 8, pp. 5148-5153

paper uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) to model the PV and wind sources. The algorithm developed is …

Persistent link: https://www.econbiz.de/10010810182

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Kisi, Ozgur; Shiri, Jalal - In: Water Resources Management 25 (2011) 13, pp. 3135-3152

precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neuro-fuzzy (NF) models are …

Persistent link: https://www.econbiz.de/10010794379

Intraday high-frequency FX trading with adaptive neuro-fuzzy inference systems

Kablan, Abdalla; Ng, Wing Lon - In: International Journal of Financial Markets and Derivatives 2 (2011) 1/2, pp. 68-87

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict …

Persistent link: https://www.econbiz.de/10010670187

Flow regimes identification and liquid-holdup prediction in horizontal multiphase flow based on neuro-fuzzy inference systems

El-Sebakhy, Emad A. - In: Mathematics and Computers in Simulation (MATCOM) 80 (2010) 9, pp. 1854-1866

neither perform well nor very accurate. Recently, neuro-fuzzy inference systems learning scheme have been gaining popularity … is able to forecast an output in the uncertainty situations. This paper investigates the capabilities of neuro-fuzzy … TypeI in identifying flow regimes and forecasting liquid holdup in horizontal multiphase flow. The performance of neuro-fuzzy …

Persistent link: https://www.econbiz.de/10010870117

Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting

Güldal, Veysel; Tongal, Hakan - In: Water Resources Management 24 (2010) 1, pp. 105-128

Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources...

Persistent link: https://www.econbiz.de/10010997376

MODULAR SOFT COMPUTING APPROACH FOR AIRCRAFT CARRIER LANDING TRAJECTORY PREDICTION

TSENG, CHRIS; EPSHTEYN, ARKADY - In: New Mathematics and Natural Computation (NMNC) 05 (2009) 02, pp. 497-512

carriers. NeuroFuzzy systems are used to emulate the flight patterns for future real-time flight prediction. To improve the … categories in accordance to their physical structure. Each module of data is presented to a different NeuroFuzzy system for …

Persistent link: https://www.econbiz.de/10004980436

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing

Pinthong, Panuwat; Gupta, Ashim Das; Babel, Mukand; … - In: Water Resources Management 23 (2009) 4, pp. 697-720

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a … multipurpose reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system … combines the gradient descent and the least-square methods to train the genetic-based neurofuzzy network by adjusting the …

Persistent link: https://www.econbiz.de/10010794480

Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems

Chaabene, Maher; Ben Ammar, Mohsen - In: Renewable Energy 33 (2008) 7, pp. 1435-1443

) gives a daily meteorological behaviour. It consists of a neuro-fuzzy estimator based on meteorological parameters …

Persistent link: https://www.econbiz.de/10010803977

Modeling a ground-coupled heat pump system by a support vector machine

Esen, Hikmet; Inalli, Mustafa; Sengur, Abdulkadir; … - In: Renewable Energy 33 (2008) 8, pp. 1814-1823

This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for...

Persistent link: https://www.econbiz.de/10010804892