潜艇垂直面运动自适应神经网络模糊控制仿真

神经网络控制和模糊控制技术的广泛应用为潜艇自动舵控制器的设计提供了新的思路.而模糊规则的提取和隶属函数的学习是模糊推理系统设计中重要而困难的问题,自适应神经网络模糊推理系统(ANFIS)结合模糊控制和神经网络控制的优点,基于sugeno模糊模型采用反向传播法和最小二乘法调整模糊推理系统的参数,并自动产生模糊规则.利用方法对潜艇乖直面运动自动舵控制器进行了设计和仿真.从仿真结果来看,自适应神经网络模糊控制器能较好的实现对潜艇垂直面运动的操纵控制,是一种很好的控制方法.     

基于ANFIS的非线性电机系统的建模

将一种神经—模糊结构—自适应神经模糊推理系统 (简称ANFIS)用于非线性电机系统的建模 ,获得了一个良好的大范围的全局非线性模型 ,同时 ,通过与反向传播网络建模结果的性能对比 ,说明ANFIS在参数收敛速度及建模精度上的优越性。显示出ANFIS是非线性系统的建模、辨识的有力工具     

Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems

Abstract: A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.     

基于ANFIS的非线性系统辨识研究

系统辨识是控制系统设计的基础，对非线性系统进行辨识是当前的难点；文献[1]提出了用模糊建模方法，文献[2]提出了用神经网络方法，在总结上述方法不足的基础上，该文提出了用自适应神经模糊推理系统(ANFIS)对非线性系统进行辨识的方法，仿真结果表明，ANFIS进行非线性系统辨识是可行的，其辨识精度很高。     

智能控制在城市交通控制中的应用

介绍了自适应神经模糊推理系统的结构,以及用MATLAB模糊工具箱提供的ANFIS应用工具仿真,完成训练模糊神经网络,将智能控制应用在城市交通控制中。     

基于模糊神经网络的温度控制系统研究

在硬件系统不变的情况下提出一种新型温度控制方案,结合自适应模糊控制和神经网络,用神经网络的学习能力计算出隶属度函数参数及相应的模糊规则,达到更高的控制精度。并运用Matlab中自适应神经网络模糊推理系统ANFIS对系统进行了仿真,研究表明系统具有极强的适应能力和稳定性。     

色织生产调度中基于ANFIS的整经轴数智能预测

分析了整经轴数预测在实际色织生产过程调度中所起的重要作用.由于与整经轴数相关的工艺属性较多且关系复杂,一般的神经网络法难以确定工艺属性与整经轴数之间关系或应用效果不佳,同时神经网络是一个"黑箱",从中无法对影响整经轴数的因素进行分析,因此提出了基于ANFIS的整经轴数智能预测方法.在该方法中,采用ANFIS作为整经轴数的预测技术,并针对ANFIS输入变量维数较大的问题,提出了一种基于模糊C均值聚类算法的ANFIS结构辨识方法.将该预测方法用于实际的色织生产过程调度的整经轴数预测中,实验结果表明该预测方法是有效的.     

粗糙集与模糊神经网络集成在故障诊断中的研究

考虑模糊聚类的数据离散功能,粗糙集理论对决策系统的约简能力,以及模糊神经网络在模式识别方面具有的优势,提出了粗糙集一自适应模糊神经网络推理系统（ANFIS）集成进行故障诊断的方案：首先,应用SOM方法离散故障诊断数据中的连续属性值;然后,基于粗糙集理论计算诊断决策系统的约简,按照实际需要确定诊断条件;最后,根据系统约简设计ANFIS进行故障诊断。4135柴油机的实际诊断结果验证了文中提出集成故障诊断方案的可行性。在数据充分的条件下,该方案可以推广应用于其它机械设备。     

An adaptive neuro-fuzzy model for prediction of student’s academic performance

This paper introduces a systematic approach for the design of a fuzzy inference system based on a class of neural networks to assess the students’ academic performance. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, there is an increasing trend to expand them with learning and adaptation capabilities through combinations with other techniques. Fuzzy systems-neural networks and fuzzy systems-genetic algorithms are the most successful applications of soft computing techniques with hybrid characteristics and learning capabilities. The developed method uses a fuzzy system augmented by neural networks to enhance some of its characteristics like flexibility, speed, and adaptability, which is called the adaptive neuro-fuzzy inference system (ANFIS). New trends in soft computing techniques, their applications, model development of fuzzy systems, integration, hybridization and adaptation are also introduced. The parameters set to facilitate the hybrid learning rules for the constitution of the Sugeno-type ANFIS architecture is then elaborated. The method can produce crisp numerical outcomes to predict the student’s academic performance (SAP). It also provides an alternative solution to deal with imprecise data. The results of the ANFIS model are as robust as those of the statistical methods, yet they encourage a more natural way to interpret the student’s outcomes.     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

应用自适应神经模糊推理系统(ANFIS)进行建模与仿真

模糊规划的提取和隶属度函数的学习是模糊推理系统设计中重要而困难的问题，自适应神经模糊推理系统（ANFIS）方法基于Sugeno模糊模型，其结构类似于神经网络，采用反向传播算法和最小二乘法调整模糊推理系统的参数，并能自动产生模糊规划，本文应用该方法给出了对一个典型系统建模的仿真实例，取得了良好的效果。     

Prediction of ground motion parameters using randomized ANFIS (RANFIS)

In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies.     

Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment

Driving a car and piloting an airplane are the most common examples for manual control of complicated processes. Human operators are known to be nonlinear, adaptive, time varying and intelligent controllers. In some cases, the human operator may or may not be well trained or an expert, showing different dynamics from operator to operator as in driving example. Therefore, it is very difficult to obtain mathematical models of human operators in a human-in-the-loop-manual control tasks. The goal of this research is to find a simple dynamic model for the prediction of the human operator actions in a manual control system. A computer-based experiment has been designed using the system identification theory to collect data from human operators. The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as an intelligent modeling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of human operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model.     

ANFIS-based approach for predicting sediment transport in clean sewer

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r² = 0.98 and RMSE = 0.002431) compared to the existing predictor.     

Computation of resonant frequency for equilateral triangular microstrip antennas using the adaptive neuro-fuzzy inference system

A new method based on the adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of the equilateral triangular microstrip patch antenna is presented. The ANFIS has the advantages of the expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid-learning algorithm, which combines the least-square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The results of the new method show better agreement with the experimental results, as compared to the results of previous methods available in the literature. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 134–143, 2004.     

Adaptive Decoupling Control of Pulp Levels in Flotation Cells

Control of the pulp levels in flotation cells directly affects the grade of the concentrate and the tailings in a concentration plant. Nevertheless, with strong coupling among cell levels and nonlinearities in the flotation process, conventional control strategies cannot achieve satisfactory control performance. In this paper, a nonlinear multi‐model adaptive decoupling control strategy based on adaptive‐network‐based fuzzy inference systems (ANFIS) is proposed for the flotation process, which includes a linear adaptive decoupling controller, an ANFIS‐based nonlinear adaptive decoupling controller, and a switching mechanism. The proposed method not only improves the transient performance and mitigates effects of the nonlinearities on the system, but also guarantees the input‐output stability of the closed‐loop system. Successful application to the flotation process has been made in a concentration plant in China, and the feasibility and efficiency of the proposed method have been validated.     

纯碱碳化过程建模研究

针对纯碱碳化过程复杂、建模难的特点,提出一种基于T-S模型的自适应神经模糊推理系统（ANFIS）建模方法。该方法提取碳化过程塔内温度分布及出碱流量的实测数据,通过ANFIS网络自组初始化模糊规则,自适应调整前提隶属度参数和结论参数,最终建立出碱流量随塔内温度变化的非线性模型。文章讨论了该网络的结构和学习算法,通过仿真研究得出其良好的实用价值。     

MODELING AND CONTROL OF INTERNAL COMBUSTION ENGINES USING INTELLIGENT TECHNIQUES

This article will compare two different fuzzy-derived techniques for controlling small internal combustion engine and modeling fuel spray penetration in the cylinder of a diesel internal combustion engine. The first case study is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second case study used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is affected by a neural networks based on prior knowledge. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters. Future work is concentrating on the establishment of an improved neuro-fuzzy paradigm for adaptive, fast and accurate control of small internal combustion engines.     

An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver

Impulsive noise is one of the main disturbances that damage the data transmission over power-line communication (PLC) systems. This paper presents an adaptive noise cancellation approach based on the adaptive neuro-fuzzy inference system (ANFIS) and a chaotic interleaver, namely ANC-CI-ANFIS scheme for impulsive noise estimation and suppression from the OFDM PLC channel. The ANFIS is based on a hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference system. Accordingly, fuzzy membership function parameters are trained using a combination of both least-square and back propagation gradient descent algorithms to emulate a given training data set. Furthermore, transmitted data are managed with a chaotic interleaver to secure data transmission and give more robustness against impulsive bursts. Simulation results are carried out on an OFDM PLC transmission chain compatible with the HomePlug AV standard under different impulsive noise scenarios. The results demonstrated the scheme's ability to detect and remove the impulsive noise from the PLC channel while keeping a high security level by using the chaotic interleaver. The major advantage of this system is its ease of implementation and faster convergence rate.     

基于T-S模型的锌钡白干燥煅烧过程自适应神经模糊推理系统建模

针对锌钡白干燥煅烧过程建模难的问题，提出了一种基于T-S模型的自适应神经模糊推理系统(ANFIS)建模方法．通过对模糊辨识系统的结构辨识和参数辨识，使网络自主、迅速地收敛到要求的输入输出关系．文章讨论了该网络的结构和学习算法，并通过仿真研究得出其良好的实际应用价值．