• Title/Summary/Keyword: ANFIS

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A Study on Fuzzy Wavelet Neural Network System Based on ANFIS Applying Bell Type Fuzzy Membership Function (벨형 퍼지 소속함수를 적용한 ANFIS 기반 퍼지 웨이브렛 신경망 시스템의 연구)

  • 변오성;조수형;문성용
    • Journal of the Institute of Electronics Engineers of Korea TE
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    • v.39 no.4
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    • pp.363-369
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    • 2002
  • In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

Runoff estimation using modified adaptive neuro-fuzzy inference system

  • Nath, Amitabha;Mthethwa, Fisokuhle;Saha, Goutam
    • Environmental Engineering Research
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    • v.25 no.4
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    • pp.545-553
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    • 2020
  • Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

The Control of a Bipedal Robot using ANFIS (ANFIS를 이용한 이족보행로봇 제어)

  • Hwang, Jae-Pil;Kim, Eun-Tai;Park, Mignon
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.523-525
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    • 2004
  • Over the last few years, the control of bipedal robot has been considered a promising research field in the community of robotics. But the problems we encounter make the control of a bipedal robot a hard task. The complicated link connection of the bipedal robot makes it impossible to achieve its exact model. In addition, the joint velocity is needed to accomplish good control performance. In this paper a control method using ANFIS as an system approximator is purposed. First a model biped robot of a biped robot with switching leg influence is presented. Unlike classical method, ANFIS approximation error estimator is inserted in the system for tuning the ANFIS. In the entire system, only ANFIS is used to approximate the uncertain system. ANFIS tuning rule is given combining the observation error, control error and ANFIS approximation error. But this needs velocity information which is not available. So a practical method is newly presented. Finally, computer simulation results is presented to show this control method has good position tracking performance and robustness without need for leg switching acknowledgement.

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The Application of Adaptive Network-based Fuzzy Inference System (ANFIS) for Modeling the Hourly Runoff in the Gapcheon Watershed (적응형 네트워크 기반 퍼지추론 시스템을 적용한 갑천유역의 홍수유출 모델링)

  • Kim, Ho Jun;Chung, Gunhui;Lee, Do-Hun;Lee, Eun Tae
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.31 no.5B
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    • pp.405-414
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    • 2011
  • The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification

  • Hakim, S.J.S.;Razak, H. Abdul
    • Structural Engineering and Mechanics
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    • v.45 no.6
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    • pp.779-802
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    • 2013
  • In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets

  • Kar, Swapnasarit;Biswal, K.C.
    • Computers and Concrete
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    • v.26 no.5
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    • pp.397-409
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    • 2020
  • The current study targets to estimate the contribution of the end-anchored FRP composites in resisting shear force using a soft computing tool i.e., adaptive neuro-fuzzy inference system (ANFIS). A total of 107 sets of data accumulated from literature was utilized for the development and evaluation of the current ANFIS model. A comparative analysis between the ANFIS predictions and the acquired experimental results has shown that the ANFIS predictions are in very good agreement with that of experimental ones. Additionally, the accuracy of the current ANFIS model has been weighed up against the estimates of nine widely adopted design guidelines. Based on various statistical parameters, it has been deduced that the effectiveness of the current ANFIS model is better than the considered design guidelines. Besides this, a parametric study was carried out to explore the combined effect of different parameters as well as the impact of individual parameters.

Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control

  • Kim, Sung-Woo;Park, Sang-Young;Park, Chan-Deok
    • Journal of Astronomy and Space Sciences
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    • v.29 no.4
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    • pp.389-395
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    • 2012
  • The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

Effects of infill walls on RC buildings under time history loading using genetic programming and neuro-fuzzy

  • Kose, M. Metin;Kayadelen, Cafer
    • Structural Engineering and Mechanics
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    • v.47 no.3
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    • pp.401-419
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    • 2013
  • In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

Design and Evaluation of ANFIS-based Classification Model (ANFIS 기반 분류모형의 설계 및 성능평가)

  • Song, Hee-Seok;Kim, Jae-Kyeong
    • Journal of Intelligence and Information Systems
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    • v.15 no.3
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    • pp.151-165
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    • 2009
  • Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

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Vector Control System for Induction Motor using ANFIS Controller (ANFIS Controller틀 이용한 유도전동기 벡터제어 시스템)

  • Lee, Hak-Ju
    • Proceedings of the KIEE Conference
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    • 2006.07b
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    • pp.1051-1052
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    • 2006
  • This paper deals with mathmatical of an induction motor, considering non-linearity in the torque balance equation under closed loop operation with a reference speed. A controller based on Adaptive Nuro-Fuzzy Inference System (ANFIS) is developed to minimize overshoot and settling time following sudden changes in load torque. The overall system is modeled and simulated using the Matlab/simulink and Fuzzy Logic Toolbox. The advantages of fuzzy logic and neural network based fuzzy logic controller. Required training data the ANFIS controller is generated by simulation of the anti-windup PI controller is eliminated using the ANFIS controller. The transient deviation of the response from the set reference following variation in load torque is found to be negligibly samll along with a desirable reduction in settling time for the ANFIS controller.

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