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Journal articles on the topic 'Adaptive Network Based Fuzzy Inference System (ANFIS)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Jang, J. S. R. "ANFIS: adaptive-network-based fuzzy inference system." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 3 (1993): 665–85. http://dx.doi.org/10.1109/21.256541.

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CHAI, YUANYUAN, and LIMIN JIA. "CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION." International Journal of Computational Intelligence and Applications 10, no. 01 (2011): 15–34. http://dx.doi.org/10.1142/s1469026811002970.

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In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neura

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3

Gao, Ming Ming, and Liang Shan. "The Study of System Model Based on Fuzzy Inference and Neural Network." Applied Mechanics and Materials 197 (September 2012): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.197.547.

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For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. Thi

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Yeom, Chan-Uk, and Keun-Chang Kwak. "Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach." Applied Sciences 10, no. 23 (2020): 8495. http://dx.doi.org/10.3390/app10238495.

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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, m

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Siddiquee, Mahfuzur Rahman, Naimul Haider, and Rashedur M. Rahman. "Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System." International Journal of Fuzzy System Applications 4, no. 4 (2015): 31–69. http://dx.doi.org/10.4018/ijfsa.2015100103.

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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Dist

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EL-BAKRY, M. Y. "A STUDY OF K–P INTERACTION AT HIGH ENERGY USING ADAPTIVE FUZZY INFERENCE SYSTEM INTERACTIONS." International Journal of Modern Physics C 15, no. 07 (2004): 1013–20. http://dx.doi.org/10.1142/s0129183104006467.

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Adaptive Network Fuzzy Inference System (ANFIS) is an artificial intelligence (AI)-based technique that proved efficient in a variety of problems such as classification, recognition and modeling of complex systems. This paper utilizes the adaptive network fuzzy inference system to model the K–P interactions. The ANFIS-based K–P model simulates the multiplicity distribution of charged pions at different high energies. The results showed very accurate fitting to the experimental data recommending it to be a good alternative to other theoretical techniques.

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Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated control

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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.

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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intellige

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptatio

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Rathnayake, Namal, Upaka Rathnayake, Tuan Linh Dang, and Yukinobu Hoshino. "A Cascaded Adaptive Network-Based Fuzzy Inference System for Hydropower Forecasting." Sensors 22, no. 8 (2022): 2905. http://dx.doi.org/10.3390/s22082905.

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Hydropower stands as a crucial source of power in the current world, and there is a vast range of benefits of forecasting power generation for the future. This paper focuses on the significance of climate change on the future representation of the Samanalawewa Reservoir Hydropower Project using an architecture of the Cascaded ANFIS algorithm. Moreover, we assess the capacity of the novel Cascaded ANFIS algorithm for handling regression problems and compare the results with the state-of-art regression models. The inputs to this system were the rainfall data of selected weather stations inside t

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A-Matarneh, Feras Mohammed, Bassam A. Y. Alqaralleh, Fahad Aldhaban, et al. "Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks." Computational Intelligence and Neuroscience 2022 (May 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/7940895.

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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such

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Ouyang, Huei-Tau. "Characteristics of adaptive network-based fuzzy inference system for typhoon inundation level forecast." Hydrology Research 49, no. 4 (2017): 1056–71. http://dx.doi.org/10.2166/nh.2017.009.

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Abstract Heavy rainfall brought in by a typhoon often causes severe inundation in a low-lying area. Due to budget constraints, inundation level monitoring programs often cease to continue after the project ends. In such cases, forecast models capable of predicting inundation levels solely based on rainfall data to provide supportive information for responding actions during typhoons are urged. This paper aims to explore two types of typhoon inundation level forecast models based on adaptive network-based fuzzy inference system (ANFIS): one employing only rainfall data as inputs (ANFIS-R) to co

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Zhang, X. Y., and B. Wei. "A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1207–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1207-2020.

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Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of A

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Ranković, Vesna, Jasna Radulović, Ivana Radojević, Aleksandar Ostojić, and Ljiljana Čomić. "Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system." Journal of Hydroinformatics 14, no. 1 (2011): 167–79. http://dx.doi.org/10.2166/hydro.2011.084.

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Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gru

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Shahbudin, Shahrani, Murizah Kassim, Roslina Mohamad, Saiful Izwan Suliman, and Yuslinda Wati Mohamad Yusof. "Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1196. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1196-1202.

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This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classiﬁcation tasks using extracted features based on S-transforms methods. The ANFIS model with a ﬁve-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that

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Nireekshana, Namburi, R. Ramachandran, and G. V. Narayana. "Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation." E3S Web of Conferences 472 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202447202005.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested A

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Ying-Ji Liu, Ying-Ji Liu, Qi-Hang Wang Ying-Ji Liu, Hai-Ying Xia Qi-Hang Wang, Xin-Lei Wei Hai-Ying Xia, Hong Jia Xin-Lei Wei, and Guo-Liang Dong Hong Jia. "Research for Fault Diagnosis Method and System for Diesel Engine Based on ANFIS." 電腦學刊 33, no. 1 (2022): 179–88. http://dx.doi.org/10.53106/199115992022023301016.

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<p>After compliance verification, operating vehicles can enter the road transportation market. Diesel engine is the main power source of these vehicles, there will be some typical faults during the use of diesel engine, which will affect the technical status of vehicles. According to the fault diagnosis problem of diesel engine, a fault diagnosis method based on Adaptive-Network-Based Fuzzy Inference System(ANFIS) was proposed, Subtractive clustering algorithm was used to confirm the original structure of fuzzy inference model, and ANFIS was used to build an original fault diagnosis mode

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Zhang, Hui Ying. "Modeling Permanent Magnet Synchronous Motor System in Electrical Automation Engineering Based on Adaptive Neuro-Fuzzy Inference System." Advanced Materials Research 676 (March 2013): 297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.676.297.

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This paper proposes a novel modeling method for permanent magnet synchronous motor (PMSM) system in electrical automation engineering based on adaptive network based fuzzy inference system (ANFIS). Meanwhile the microhabitat particle swarm optimization (MPSO) was used for training the parameters of ANFIS. The proposed modeling method for PMSM system can help in speed and position control. The proposed ANFIS and MPSO based modeling method has been successfully applied to PMSM control system.

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Kotkova, E. "Neural network model for predicting pre-evacuation behavior of people in case of fire." National Security and Strategic Planning 2022, no. 2 (2022): 66–72. http://dx.doi.org/10.37468/2307-1400-2022-2-66-72.

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This article discusses a comprehensive study of pre-evacuation behavior of people in the event of an emergency. In this regard, it is advisable to use machine learning approaches, in particular neural networks, for data mining in the field of security. Statistical data obtained in emergency situations may be limited and generally uncertain, for this reason it is recommended to choose a neural network architecture - adaptive fuzzy inference Network System (ANFIS) based on the Takagi–Sugeno fuzzy inference system. The neural network architecture considered in the article in the form of an adapti

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Oudah, Manal Kadhim, Salam Waley Shneen, and Suad Ali Aessa. "Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System." International Journal of Robotics and Control Systems 5, no. 2 (2025): 678–97. https://doi.org/10.31763/ijrcs.v5i2.1684.

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Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neu

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Li, Meng Jia, Jing Yao Wang, Mei Song, Xiao Jun Wang, and Ning Ning Liu. "An Wavelet Based Handoff Algorithm for Cognitive Wireless Network." Advanced Materials Research 433-440 (January 2012): 5087–91. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5087.

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This paper proposed a novel handoff algorithm for cognitive network based on wavelet analysis and fuzzy control system. It makes the system cognitive and adaptive to the changes of the environment by two steps: first, make wavelet analysis to the received signal to get the basic signal which is without noise. Second, use adaptive neuro-fuzzy inference system (ANFIS) to make diligent handoff decision. The simulation shows that it improves the performance of the whole system when the channel is in low signal-to-noise ratio.

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Meesaraganda, L. V. Prasad, and Prasenjit Saha. "Adaptive Neuro-Fuzzy Inference System for Predicting Compressive Strength of Fibres Self Compacting Concrete." Applied Mechanics and Materials 892 (June 2019): 46–54. http://dx.doi.org/10.4028/www.scientific.net/amm.892.46.

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This research focused on the applicability of Adaptive Network-Based Fuzzy Inference System (ANFIS) for predict the compressive strength of fibers self-compacting concrete. An ANFIS model combines the benefit of ANN and fuzzy logic. The data developed experimentally for fibers self-compacting concrete and the data sets of a total 99 concrete samples were used in this work. In this paper research is computational based for prediction of concrete compressive strength. A model was developed using ANFIS with five input nodes as w/p ratio, course aggregate, fine aggregate, fiber and superplastizers

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Nguyen, Hoang-Long, Binh Thai Pham, Le Hoang Son, et al. "Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction." Applied Sciences 9, no. 21 (2019): 4715. http://dx.doi.org/10.3390/app9214715.

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The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN)

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Thirunavukkarasu, M., S. Sathish, V. Rahul, R. Vinoth, S. Ram, and O. Akbar Basha. "Hybrid adaptive network-based fuzzy inference system Model for Biodiesel Production in Renewable System." E3S Web of Conferences 619 (2025): 01010. https://doi.org/10.1051/e3sconf/202561901010.

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Biodiesel is used which positively impact the environment or reducing the dependence on fossil fuels while providing a viable alternative. This paper presents the use of machine learning approach, namely adaptive neuro-fuzzy inference system (ANFIS) to optimize and model the biodiesel production from combination of soya oil and waste cooking oil. The effect of the process parameters catalyst value (5-7 wt. %), Methnol/soya +waste cooking oil ratio (10-20) , and react time (20–40 min) were studied. after optimizing the reaction parameters, bio diesel production (BDP) of 95.8 % was achieved whil

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Zeinali, Mohammadjavad, Saiful Amri Mazlan, Abdul Yasser Abd Fatah, and Hairi Zamzuri. "A GA-Weighted Adaptive Neuro-Fuzzy Model to Predict the Behaviour of Magnetorheological Damper." Applied Mechanics and Materials 663 (October 2014): 203–7. http://dx.doi.org/10.4028/www.scientific.net/amm.663.203.

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Magnetorheological damper is a controllable device in semi-active suspension system to absorb unwanted movement. The accuracy of magnetorheological damper model will affect performance of the control system. In this paper, a combination of genetic algorithm (GA) and adaptive-network-based fuzzy inference system (ANFIS) approaches is utilized to model the magnetorheological damper using experimental results. GA algorithm is implemented to modify the weights of the trained ANFIS model. The proposed method is compared with ANFIS and artificial neural network (ANN) methods to evaluate the predicti

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Nawaz, Nadeem, Sobri Harun, and Amin Talei. "Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) for River Stage Prediction in a Tropical Catchment." Applied Mechanics and Materials 735 (February 2015): 195–99. http://dx.doi.org/10.4028/www.scientific.net/amm.735.195.

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Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and sta

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Xu, Tian Lai, and Yang Tian. "Research on Integrated Navigation Algorithm Based on ANFIS." Applied Mechanics and Materials 88-89 (August 2011): 438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.438.

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Combination of Global Positioning System (GPS) and Inertial Navigation System (INS) can improve the navigation performance that is superior to either one. This paper proposed and discussed an INS/GPS integrated navigation method based on adaptive neuro-Fuzzy Inference System (ANFIS) to fuse INS and GPS data. In this method, an ANFIS network was trained to mimic the error dynamical model of INS when GPS signals were available. If GPS outages occur, the trained ANFIS network is utilized to bridge the GPS outages. Simulations in INS/GPS integrated navigation system show the proposed method can re

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Pourtousi, M., Mohammadjavad Zeinali, P. Ganesan, and J. N. Sahu. "Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS." RSC Advances 5, no. 104 (2015): 85652–72. http://dx.doi.org/10.1039/c5ra11583c.

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This work presents a combination of Computational Fluid Dynamics (CFD) and Adaptive Network-based Fuzzy Inference System (ANFIS) developed for flow characterization inside a cylindrical bubble column reactor.

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Ansaf, Huda, Bahaa Kazem Ansaf та Sanaa S. Al Samahi. "A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus". BioChem 1, № 3 (2021): 159–73. http://dx.doi.org/10.3390/biochem1030013.

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This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership funct

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Mon, Yi-Jen. "Airbag controller designed by adaptive-network-based fuzzy inference system (ANFIS)." Fuzzy Sets and Systems 158, no. 24 (2007): 2706–14. http://dx.doi.org/10.1016/j.fss.2007.06.005.

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Tiab Ali. "Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network." Panamerican Mathematical Journal 35, no. 1s (2024): 103–14. http://dx.doi.org/10.52783/pmj.v35.i1s.2119.

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The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a mul

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Jing, Guo Lin, Wen Ting Du, Xiang Chen, and Huan Yi. "Prediction Model in Electrodialysis Process Based on ANFIS." Advanced Materials Research 268-270 (July 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.332.

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Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to predict separation percent(SP) of NaCl solution as a function of concentration, temperature, flow rate and voltage. Besides, in the MATLAB, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically. We obtained fitted values of SP by ANFIS. Then, we studied these influencing factors on fitted values of SP. Finally, we draw a conclusion that SP is in direct proportion to temperature and voltage, but in

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Shekofteh, Hosein, Majid Afyuni, Mohammad Ali Hajabbasi, et al. "Nitrate leaching from a potato field using adaptive network-based fuzzy inference system." Journal of Hydroinformatics 15, no. 2 (2012): 503–15. http://dx.doi.org/10.2166/hydro.2012.075.

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The conventional methods of application of nitrogen fertilizers might be responsible for the increased nitrate concentration in groundwater of areas dominated by irrigated agriculture. Appropriate water and nutrient management strategies are required to minimize groundwater pollution and to maximize nutrient use efficiency and production. Design and operation of a drip fertigation system requires understanding of nutrient leaching behavior in cases of shallow rooted crops such as potatoes which cannot extract nutrient from a lower soil depth. This study deals with neuro-fuzzy modeling of nitra

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Parhi, D. R., and M. K. Singh. "Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 6 (2010): 1369–81. http://dx.doi.org/10.1243/09544062jmes1751.

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This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include t

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Luo, Yunhui, Xingguang Wang, Qing Wang, and Yehong Chen. "Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System." Applied Sciences 11, no. 21 (2021): 9936. http://dx.doi.org/10.3390/app11219936.

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Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this

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Katiyar, Kartik, Ayush Ayush, Deep Singh, and Supriya Sharma. "POWER MANAGEMENT STRATEGY BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR AC MICROGRID." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 288–91. http://dx.doi.org/10.53633/ijmasri.2021.1.10.006.

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Micro grids increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources integrated into the MGs usually results in different problems. We are trying to achieve a stable system which can power microgrids and can be of commercial use. This would be achieved by simulation of the system under different climatic conditions. Keywords: Microgrid Optimization, ANFIS, Neural Network, Fuzzy Logic

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Zou, Zhe, Juan Chen, and Ming-Der Jean. "Predictive Modelling and Optimization of the Mechanical Properties of Laser-Coated NB/SiC/Ni Welds Using an ANFIS." Metals 14, no. 5 (2024): 585. http://dx.doi.org/10.3390/met14050585.

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In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness p

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Jing, Guo Lin, Wen Ting Du, Quan Zhou, and Song Tao Li. "Studying Fitting Effect by ANFIS in the Electrodialysis Process." Advanced Materials Research 268-270 (July 2011): 336–39. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.336.

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Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation pe

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Alavandar, Srinivasan, and M. J. Nigam. "Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators." International Journal of Computers Communications & Control 3, no. 3 (2008): 224. http://dx.doi.org/10.15837/ijccc.2008.3.2391.

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Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution requires the solution of non linear equations having transcendental functions are difficult and computationally expensive. In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS, an implementation of a representative fuzzy inference system usi

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Raj, Ajay Amrit. "Adaptive Neuro-Fuzzy Inference System-based Nonlinear Equalizer for CO-OFDM Systems." Computer Journal 63, no. 2 (2019): 169–78. http://dx.doi.org/10.1093/comjnl/bxz072.

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Abstract The principle of orthogonal frequency-division multiplexing (OFDM) is to transmit the data through a large number of multiple orthogonal subcarriers. The coherent optical OFDM (CO-OFDM) is OFDM data that are being modulated to light frequency and being detected in coherent manner. CO-OFDM brings to optical communications the combination of two powerful techniques, coherent optical detection and OFDM. One of the primary challenges in the CO-OFDM system is to remove optical fiber nonlinear effects. This makes nonlinearity compensation a critical task of the CO-OFDM system. So a nonlinea

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Madhura, Madhura, Asha KS, Mary Christeena Thomas, Anubhav Bhalla, Rajat Saini, and Aws Zuhair Sameen. "An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 99–110. http://dx.doi.org/10.54216/jisiot.130108.

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Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods:

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Dzakpasu, Mawuli, Miklas Scholz, Valerie McCarthy, Siobhán Jordan, and Abdulkadir Sani. "Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands." Water Science and Technology 71, no. 1 (2014): 22–30. http://dx.doi.org/10.2166/wst.2014.461.

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Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible pr

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Jelušič, Primož, Andrej Ivanič, and Samo Lubej. "Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System." Applied Sciences 11, no. 1 (2020): 203. http://dx.doi.org/10.3390/app11010203.

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Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The effi

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Sui, Wen Tao, and Dan Zhang. "Fault Diagnosis of Roller Bearing Conditions Using ANFIS." Applied Mechanics and Materials 16-19 (October 2009): 886–90. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.886.

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This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

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Zhao, Lai Gang, and Dao Jiong Chen. "A Fuzzy Control Method in ACC of the Constant Interval Mode." Advanced Materials Research 201-203 (February 2011): 2083–86. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2083.

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Fuzzy control methods are widely used in Adaptive Cruise Control System as time passes by,but there are also various limitations for its membership and fuzzy rule acquisition, over-relying on experts’ knowledge. This paper presented one Sugeno method based on Mamdani using ANFIS(adaptive network-based fuzzy inference system)with optimized its memberships and training processes. Its experimental results revealed that it can be embeded into ACC much more appropriately and efficiently.

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Xiang, Yi Ming, Xue Yan Liu, Gui Xiang Ling, and Bin Du. "An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions." Applied Mechanics and Materials 501-504 (January 2014): 391–94. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.391.

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An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computation

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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and labora

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Žlender, Bojan, and Primož Jelušič. "Predicting Geotechnical Investigation Using the Knowledge Based System." Advances in Fuzzy Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4867498.

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The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System (ANFIS) was proposed. The obtained fuzzy inference syste

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Kamelia, Lia, Eki Ahmad Zaki Hamidi, and Reno Muhammad Fadilla. "Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4113. http://dx.doi.org/10.11591/ijai.v13.i4.pp4113-4120.

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In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizin

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Lia, Kamelia, Ahmad Zaki Hamidi Eki, and Muhammad Fadilla Reno. "Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4113–20. https://doi.org/10.11591/ijai.v13.i4.pp4113-4120.

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In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizin

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