Results 191 to 200 of about 15,321 (232)

IT2 TSK NSFLS2 ANFIS

2010 Ninth Mexican International Conference on Artificial Intelligence, 2010
This article presents a novel learning methodology based on the hybrid mechanism for training interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training and prediction processes, the interval type-2 non-singleton type-2 TSK FLS the consequent parameters are ...
Gerardo M. Mendez, María de los Angeles Hernandez Medina   +1 more
openaire   +1 more source

Estimating Evaporation Using ANFIS

Journal of Irrigation and Drainage Engineering, 2006
Water resources engineering assessment requires simple but effective evaporation estimation procedures, especially from readily measurable meteorological factors. Unfortunately, such approaches are rather scarce in the literature. In this paper, an adaptive neural-based fuzzy inference system (ANFIS) was applied to daily meteorology data from the Lake ...
KESKİN, Mustafa Erol, TAYLAN, Emine Dilek, Terzi, Özlem   +2 more
openaire   +2 more sources

An Approach to ANFIS Performance

2015
The paper deals with Adaptive neuro-fuzzy inference system (ANFIS) and its performance. Firstly, ANFIS is described as a hybrid system based on fuzzy logic/sets and artificial neural networks. Subsequently, modifications of ANFIS are proposed. The aim of these modifications is to improve performance, accuracy or reduce computational time.
Stepan Dalecky, Frantisek Vítezslav Zboril   +1 more
openaire   +1 more source

Adaptive hybrid ANFIS-PSO and ANFIS-GA approach for occupational risk prediction

International Journal of Occupational Safety and Ergonomics
This study attempted to optimize the adaptive neuro-fuzzy inference system (ANFIS) using particle swarm optimization (PSO) and a genetic algorithm (GA) for calculating occupational risk. Numerous studies have shown that the ANFIS is a good approach for predicting engineering problems. However, it is not well investigated in the area of risk assessment.
Mourad Achouri, Youcef Zennir, Cherif Tolba   +2 more
openaire   +2 more sources

Multisensors signal processing using ANFIS

2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2013
An improved algorithm of intelligent data processing by integrating the modified identification method of individual characteristic curve along with the adaptive neuro-fuzzy inference system (ANFIS) was proposed. The results of data prediction with training the ANFIS system and the different number of training epochs are described.
Nataliya Roshchupkina, Anatoly Sachenko, Olekxiy Roshchupkin, Volodymyr Kochan, Radislav Smid   +4 more
openaire   +1 more source

A novel training algorithm in ANFIS structure

2006 American Control Conference, 2006
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO).
Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki-Sedigh   +2 more
openaire   +1 more source

Wind power prediction analysis by ANFIS, GA-ANFIS and PSO-ANFIS

Journal of Information and Optimization Sciences, 2022
Neeraj Kumar, K. Sudha, Kusum Tharani
openaire   +1 more source

Extreme learning ANFIS for control applications

2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), 2014
This paper proposes a new neuro-fuzzy learning machine called extreme learning adaptive neuro-fuzzy inference system (ELANFIS) which can be applied to control of nonlinear systems. The new learning machine combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive neuro ...
G. N. Pillai, Jagtap Pushpak, M. Germin Nisha   +2 more
openaire   +1 more source

Prediction of cutting force by using ANFIS

International Journal of System Assurance Engineering and Management, 2018
The aim of this research is to develop a model to predict the cutting forces of a turning operation. This paper focuses on to design a monitoring system that can recognize cutting force on the basis of cutting parameters like spindle speed, feed and depth of cut by using adaptive neuro-fuzzy inference system (ANFIS).
Vineet Jain, Tilak Raj
openaire   +1 more source

Predicting injection profiles using ANFIS

Information Sciences, 2007
Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields' performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper,
Mingzhen Wei, Baojun Bai, Andrew H. Sung, Qingzhong Liu, Jiachun Wang, Martha E. Cather   +5 more
openaire   +1 more source