Modeling the free convection heat transfer in a partitioned cavity using ANFIS

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to predict the free convection in a partitioned cavity consisting of an adiabatic partition. The main focus of the present paper is to consider the effects of partition angle and Rayleigh number variation on average heat transfer in the partitioned cavity. The training data for optimizing the ANFIS structure is obtained experimentally. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.055% and 1.735% respectively, which shows that ANFIS can predict the experimental results precisely.     

基于ANFIS的带材纠偏控制

带材纠偏控制系统（Edge Position Contorl,简称ECP）是带材生产线上磬不可少的重要控制系统。针对带材纠偏过程的不确定性,建立了以ANFIS为基础的带材纠偏的预测控制,仿真结果显示,该自适应神经模糊控制器具有较高的控制精度,控制效果较好。     

基于改进算法的自适应神经模糊推理系统控制的交流变频调速系统的研究与仿真

交流电动机是一个多变量、非线性对象,其静态和动态特性以及控制较之直流电动机要复杂得多。本文针对交流电动机矢量控制系统因电机参数变化和负载波动等因素而使性能变差的问题,设计了一种基于改进算法的自适应神经模糊推理系统（ANFIS）的交流电动机矢量控制系统速度调节器。仿真实验结果表明,使用自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高,而且具有较强的鲁棒性。     

Using self‐constructing recurrent fuzzy neural network as a digital channel equalizer

Abstract

In this paper the design of a self‐constructing recurrent fuzzy neural network (SCRFNN)‐based digital channel equalizer is proposed. It is found that a digital channel equalizer based on SCRFNN can recover channel distortions effectively. We compare the performance of SCRFNN with adaptive‐based‐network fuzzy inference system (ANFIS) and the Bayesian equalizers in complex‐valued linear channels. Our simulations show that the performance of SCRFNN is close to the Bayesian optimal solution. Furthermore, the hardware requirement of the trained SCRFNN equalizer is relatively lower than the other two structures.     

Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%. This paper has not been published elsewhere nor has it been submitted for publication elsewhere.     

An appraisal of wind turbine wake models by adaptive neuro-fuzzy methodology

Production losses and increased turbine loadings are observed in wind farms, when wind turbines interact with each other. If a wind turbine is located in the wake of another one, its incoming flow is disturbed, slowed down, and its potential wind power is decreased. It is therefore necessary to study the wind turbine wakes and their interactions. It is important to consider these wake effects in the design of a wind farm in order to maximize the energy output and lifetime of the machines. The exact modeling of the wind speed distribution within a wind park is a fairly complicated task and many of the necessary parameters are not routinely available. A large number of studies have been established concerning the calculation of wake effect. Even though a number of mathematical functions have been proposed, there are still disadvantages of the models like very demanding in terms of calculation time. Artificial neural networks (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the wake power deficit. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.     

基于PCA—ANFIS的油井注水有效周期预测研究

针对油田上注水有效周期不确定的问题,提出一个基于混合主成分分析与ANFIS的数值方法进行油井注水有效周期的预测研究．首先使用主成分分析对原始数据进行降维处理,然后应用ANFIS对降维后的数据进行训练与测试．实验使用油田上116口油井的真实注水统计数据检验混合主成分分析与ANFIS模型的正确性,测试的注水有效周期平均绝对误差为1．80个月,而未经过主成分分析处理的测试平均误差为4．33个月,混合主成分分析与ANFIS模型的测试精度得到大幅度提高,说明主成分分析与ANFIS的混合方法对预测油井注水有效周期是可行与有效的．     

Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics

The aim of this study was to compare the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the wrinkle recovery of polyester/cotton woven fabrics. The prediction models were developed using experimental data-set of 115 fabric samples of different constructions. Warp and weft yarn linear densities, ends/25 mm and picks/25 mm, were used as input/predictor variables, and warp and weft crease recovery angles (CRA) as output/response variables. It was found that the prediction accuracy of the ANN models was slightly better as compared with that of ANFIS models developed in this study. However, the ANFIS models could characterize the relationships between the input and output variables through surface plots, which the ANN models could not. The developed models may be used to optimize the fabric construction parameters for maximizing the wrinkle recovery of polyester/cotton woven fabrics.     

Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68?×?10^?5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.     

Synchronization controller for a 3-R planar parallel pneumatic artificial muscle (PAM) robot using modified ANFIS algorithm

Parallel manipulator is a closed-kinematic chain mechanism in which performance of its end effector – moving platform is contributed by its independent actuators. In traditional designs, each elemental actuator has its own controller as well as reference input, and it works independently without gathering information from its neighbors. Consequently, as one of the actuators cannot keep up with the others, the platform performance is easily deteriorated due to the lack of coherence between these actuators. Therefore, the aim of this paper is to design a 3-R planar parallel robot and develop a proper synchronization controller for its tracking control task. Adaptive Network Based Fuzzy Inference System (ANFIS) algorithm was modified and applied as the main strategy of this synchronization controller. The controller is then able to compensate errors between the actuators and enforce them to cooperate harmonically with each other regardless external disturbances caused by the outside environment or geometrical constraints of the closed-loop structure. Simulations and practical experiments on a scaled parallel robot were carried out to evaluate the designed controller. The results showed that by applying the proposed control technique, the working errors of the component actuators converged quickly to zero almost at the same time. As a result, the tracking performance of the common platform was significantly improved in comparison with the performance when applying a non-synchronization controller. The proposed method is effective in controlling systems which require collaborations between the sub-agents.