Results 151 to 160 of about 6,025 (187)

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

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

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

ANFIS system for classification of brain signals

Journal of Intelligent & Fuzzy Systems, 2019
Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal.
José de Jesús Rubio, David Ricardo Cruz, Israel Elias, Genaro Ochoa, Ricardo Balcazar, Arturo Aguilar   +5 more
openaire   +1 more source

Application of ANFIS to Stream-Way Transition

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
The main purpose of this paper is to predict stream-way transition with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Therefore, the downstream stream-way transition according to the upstream conditions is forecasted by ANFIS. Five main factors may affect the stream-way transition include inflow position, inflow angle, slope, flow discharge ...
Shih-Wei Ma, Chang-Huan Kou, Li Chen, An-Pei Wang, Cheng-Yuan Sung   +4 more
openaire   +1 more source

Nonlinear feedback control based on ANFIS

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
Adaptive neural fuzzy interference system (ANFIS) controller has advantages of solving the control problem of uncertain systems by means of a fuzzy system and optimizing controller parameters though the self-learning ability of a neural network. For nonlinear and uncertain ship motion, ANFIS course keeping controller is designed to deal with the ...
Xiujia Chen, Xianku Zhang
openaire   +1 more source

Application of ANFIS in the design of fuzzy controller

2012 International Conference on Machine Learning and Cybernetics, 2012
How to generate and adjust the membership function and fuzzy rules are difficult problems in the design of fuzzy controller. To solve this problem, adaptive neural fuzzy inference system (ANFIS) is used to design the fuzzy control system, then the fuzzy rules and membership function can be obtained by back propagation or hybrid algorithm of the neural ...
Teng-Fei Li, Chao-Ying Liu, Zhe-Ying Song, Xue-Ling Song, Qing-Qing Yan   +4 more
openaire   +1 more source

???????????? ???????????????? ?????????????????????? ???????????????????? ?????????? ???????????????????????? ?????????????????????? ???? ???????????? ANFIS

2014
The paper is devoted to the problem of construction the spatial distribution fields of urban probabilities based on typical tourist towns of Ukrainian Carpathian Mountains. The method of extracting data from geoinformation systems (GIS) and finding of hidden dependences using fuzzy-logic are considered.
openaire   +1 more source