An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.     

A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.     

Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets

A correct diagnosis of tuberculosis disease can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution that makes diagnosis of tuberculosis as accurate as possible and helps deciding whether it is reasonable to start tuberculosis treatment on suspected patients without waiting for the exact medical test results. We proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. Data set collected from 503 different patient records which are obtained from a private health clinic (consent of physicians and patients). Patient record has 30 different attributes which covers demographical and medical test data. ANFIS model was generated by using 250 records. Also, rough set method was implemented by using the same data set. The ANFIS model classifies the instances with correctness of 97 %, whereas rough set algorithm does the same classification with correctness of 92 %. This study has a contribution on forecasting patients before the medical tests.     

Optimization of neural networks through grammatical evolution and a genetic algorithm

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems.     

An assembly oriented design framework for product structure engineering and assembly sequence planning

The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology.     

可在线配置结构的多源融合位姿估计框架

针对组合导航系统中,融合算法结构难以在线进行配置的问题,基于误差状态扩展卡尔曼滤波器（ ES-EKF）和标准观测模型库,提出一种不依赖特定平台、可在线配置结构的多源融合估计框架,赋予组合导航系统动态变更信息源融合方式的能力。对多种导航子系统的输出信息进行分类和建模,建立一个不依赖于特定传感器和平台的标准观测模型库。设计一种算法结构的表示规则,将算法结构映射为融合模式,实现信息源和变量的灵活选择。基于该融合估计框架,设计并实现了一个惯性测量单元／磁力计／编码器／相机／激光雷达组合导航系统。最后,在野外数据集上进行了多种测试,该系统能够通过人工静态地或自主动态地变更融合模式灵活配置融合算法的结构,且定位精度优于 robot-localization 算法。试验结果表明,该框架可有效地实现多源融合估计、可在线地配置融合结构。     

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。     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

基于聚类与自适应神经模糊推理的车速预测

针对道路交通系统的非线性和随机性特点,设计一种具有学习能力的车速预测方法。首先,对交通流历史特征数据采用模糊聚类的方法进行状态分类并确立模型结构。然后,建立交通流状态预测的自适应神经模糊系统,以实测交通流数据进行系统参数优化训练。最后,利用MATLAB进行系统的仿真及检测。检测的预测结果表明系统具有良好的应用性能。     

Improved adaptive neuro-fuzzy inference system

This paper introduces a new type of Adaptive Neuro-fuzzy System, denoted as IANFIS (Improved Adaptive Neuro-fuszzy Inference System). The new structure is realized by the insertion of the error of training of ANFIS in the third layer of this system. The recurrence of the error of training will increase the capability of convergence and the robustness of ANFIS. The proposed IANFIS system is applied to make the identification of nonlinear functions, and the obtained results are compared with these obtained by usual ANFIS to verify the effectiveness of the proposed adaptive neuro-fuzzy system.     

A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs a genetic algorithm (GA) to refine the weights of rules joining in an ANFIS model to forecast the Taiwan stock index. To evaluate the forecasting performances, the proposed model is compared with four different models: Chen's model, Yu's model, Huarng's model, and the ANFIS model. The results indicate that the proposed model is superior to the listing methods in terms of the root mean squared error (RMSE).     

OSEK操作系统定时机制优化设计

OSEK/VDX操作系统标准是被汽车电子业界普遍接受的工业标准。为了满足汽车电子可靠性和实时性的要求,需要尽可能提高操作系统的定时机制对定时事件响应的速度。因此,分析定时事件在嵌入式系统中的表示,通过使用ICTOH算法、堆排序和硬件计数器对OSEK操作系统的定时机制进行优化设计。对比实验结果表明,该方法能获得更高的效率。     

Predictive Modeling of the Ti6Al4V Alloy Surface Roughness

The prediction of surface roughness is important for all materials that undergo manufacturing processes. The Ti6Al4V titanium alloy is commonly used in aerospace, automotive and power generation industries but also in the manufacturing of medical implants, mainly because of its biocompatibility. Here we study the relationship of Ti6Al4V’s surface roughness with critical machining parameters and conditions based on experimental input (machining parameters)-output (surface roughness) data derived during the turning operation. The experimental findings are converted into polynomial models through the Response Surface Methodology (RSM) and into a fuzzy logic system through the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ability of these two methodologies to predict Ti6Al4V’s surface roughness when machined is presented and compared. It is observed that the ANFIS predicts surface roughness with less error mainly when the data used for evaluation are not completely different to those used for training.     

Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system

Intrusion detection system has become the fundamental part for the network security and essential for network security because of the expansion of attacks which causes many issues. This is because of the broad development of internet and access to data systems around the world. For detecting the abnormalities present in the network or system, the intrusion detection system (IDS) is used. Because of the large volume of data, the network gets expanded with false alarm rate of intrusion and detection accuracy decreased. This is one of the significant issues when the network experiences unknown attacks. The principle objective was to expand the accuracy and reduce the false alarm rate (FAR). To address the above difficulties the proposed with Crow Search Optimization algorithm with Adaptive Neuro-Fuzzy Inference System (CSO-ANFIS) is used. The ANFIS is the combination of fuzzy interference system and artificial neural network, and to enhance the performance of the ANFIS model the crow search optimization algorithm is used to optimize the ANFIS. The NSL-KDD data set was used to validate the performance of intrusion detection of the proposed model and the experiment results are compared with other existing techniques for overall performance validation. The results of the intrusion detection based on the NSL-KDD dataset was better and efficient compared with those models because the detection rate was 95.80% and the FAR result was 3.45%.     

A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.     

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.     

ANFIS-based sensor fusion system of sit-to-stand for elderly people assistive device protocols

This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology(E-JUST) named E-JUST assistive device(EJAD).Several experiments were carried out using a motion capture system(VICON) and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units(IMUs) fused through an adaptive neuro-fuzzy inference systems(ANFIS) algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom(2-DOF) planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.     

自适应神经模糊推理系统用于症候诊断的研究

提出了基于T-S模型(Takagi-Sugeno型)的自适应神经网络模糊推理系统(AdaptiveNetwork-basedFuzzyInferenceSystem,ANFIS),介绍了高木—关野(Takagi-Sugeno型)模型结构和自适应神经网络模糊推理系统的结构和算法。该文采用减法聚类初始化模糊推理系统模型,把神经网络学习机制引入到逻辑推理中,使传统的逻辑推理不仅具有逻辑思维及语言表达能力而且具有自学习和联想能力,通过2型糖尿病症候数据库验证了ANFIS用于症候诊断的合理性和有效性。提示自适应神经网络模糊推理系统适合中医症候诊断的研究。