An improved fuzzy neural network based on T–S model

An improved fuzzy neural network based on Takagi–Sugeno (T–S) model is proposed in this paper. According to characteristics of samples spatial distribution the number of linguistic values of every input and the means and deviations of corresponding membership functions are determined. So the reasonable fuzzy space partition is got. Further a subtractive clustering algorithm is used to derive cluster centers from samples. With the parameters of linguistic values the cluster centers are fuzzified to get a more concise rule set with importance for every rule. Thus redundant rules in the fuzzy space are deleted. Then antecedent parts of all rules determine how a fuzzification layer and an inference layer connect. Next, weights of the defuzzification layer are initialized by a least square algorithm. After the network is built, a hybrid method combining a gradient descent algorithm and a least square algorithm is applied to tune the parameters in it. Simultaneous, an adaptive learning rate which is identified from input-state stability theory is adopted to insure stability of the network. The improved T–S fuzzy neural network (ITSFNN) has a compact structure, high training speed, good simulation precision, and generalization ability. To evaluate the performance of the ITSFNN, we experiment with two nonlinear examples. A comparative analysis reveals the proposed T–S fuzzy neural network exhibits a higher accuracy and better generalization ability than ordinary T–S fuzzy neural network. Finally, it is applied to predict markup percent of the construction bidding system and has a better prediction capability in comparison to some previous models.     

有混合数据输入的自适应模糊神经推理系统

现有数据建模方法大多依赖于定量的数值信息,而对于数值与分类混合输入的数据建模问题往往根据分类变量组合建立多个子模型,当有多个分类变量输入时易出现子模型数据分布不均匀、训练耗时长等问题.针对上述问题,提出一种具有混合数据输入的自适应模糊神经推理系统模型,在自适应模糊推理系统的基础上,引入激励强度转移矩阵和结论影响矩阵,采用基于高氏距离的减法聚类辨识模型结构,通过混合学习算法训练模型参数,使数值与分类混合数据对模糊规则的前后件参数同时产生作用,共同影响模型输出.仿真实验分析了分类数据对模型规则后件的作用以及结构辨识算法对模糊规则数的影响,与其他几种混合数据建模方法对比表明本文所提出的模型具有较高的预测精度和计算效率.     

基于UKF的自适应模糊推理神经网络

如何生成最优的模糊规则数及模糊规则的自动生成和修剪是模糊神经网络训练算法研究的重点。针对这一问题,本文提出了基于UKF的自适应模糊推理神经网络(UKF-ANFIS)。首先,通过减法聚类确定UKF-ANFIS的模糊规则及其高斯隶属函数的中心和宽度参数;其次,分析了模糊神经网络的非线性动力系统表示,并用LLS和UKF分别学习线性和非线性的参数;然后,用误差下降率方法作为模糊规则修剪的策略,删除作用不大的规则;最后,通过典型的函数逼近和系统辨识实例,表明本文算法得到的模糊神经网络的结构更为紧凑,泛化性能也更佳。     

A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle, 1982, Cheng et al., 2010. After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

基于ANFIS和减法聚类的遥感红外目标分选

针对自适应神经模糊推理系（ANFIS）的结构特点，本文应用多个ANFIS组合的网络来解决多目标分选问题。为了给ANFIS赋予一个合适的初始状态，减法聚类被用于预处理输入特征数据，实现了网络结构和初始参数的优化。基于ANFIS的红外航母和大型舰船目标分选仿真实验证明，其学习速度快、目标分选准确。     

Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model’s uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient “If-Then” rules.The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution.Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.     

用HCM聚类和遗传算法实现多级模糊神经网络

模糊集理论适用于一些实验数据中不确定性和模糊性的建模问题,而模糊推理系统拥有模糊IF-THEN格式的结构化知识表示,但缺少适应性。神经网络本身具有对外部很强的适应性和从过去数据中学习的机制,但基于线性推理的模糊神经网络(FNN)模型作为模糊推理方法不能得到存在于参数间的最终关系,也不能影响接着发生的模糊集合。因此,我们提出了一个多级模糊神经网络(Multi-FNN),使用硬C均值聚类和进化模糊颗粒,利用处理为近似推理的一个线性推理,获得信息微粒和模糊集之间的关系。     

基于模糊模型的结构和参数的一体化辨识

基于模糊集合的模糊建模捕述复杂、病态、非线性系统的特性是一种有效方法．文中讨论了从样本数据中通过正交变换和模糊聚类获取模糊规则的方法．利用正交最小二乘对模糊聚类的结果进行变换,采用CGS（Classical Gram—Schmidt）方法确定对建模贡献大的规则,删除对建模贡献小的规则,并对模型中的参数进行估计,能够同时模对糊模型的结构和参数进行辨识．仿真结果表明,提出的方法能够对非线性系统进行模糊建模．     

Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method

In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity defect and surface defects as the output data. The process of model building is carried out by using subtractive clustering in both the input and output spaces. A minimum error model is developed through exhaustive search of clustering parameters. The fuzzy model obtained is capable of predicting the tile defects for a given set of inputs as mentioned above. The fuzzy model is verified experimentally using different sets of inputs. This study intends to examine and deal with the experimental results obtained during various stages of ceramic tile production during 90-day period. It is believed, that the results obtained from the present study could be considered in other ceramic tiles industries, which experienced similar forms of defects.     

减法聚类-ANFIS在网络故障诊断的应用研究

提出了一种基于减法聚类-自适应模糊神经网络（ANFIS）的网络故障诊断建模方法。减法聚类算法生成初始模糊推理系统,ANFIS建立网络故障诊断原始模型,应用混合算法对模糊规则的参数进行训练并建立最终的模型。仿真实验表明基于减法聚类-ANFIS的建模方法是有效的;通过仿真结果比较,减法聚类-ANFIS的网络故障诊断能力及收敛速度均优于BP神经网络,更适合作为网络故障诊断模型。     

Design of fuzzy systems using neurofuzzy networks

Introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, non-noisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.     

A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.      

基于Nyström密度值逼近的减法聚类

针对大规模数据集减法聚类时间复杂度高的问题,提出一种基于Nyström密度值逼近的减法聚类方法。特别适用于大规模数据集的减法聚类问题,可极大程度降低减法聚类的时间复杂度。基于Nyström逼近理论,结合经典减法聚类样本密度值计算的特点,巧妙地将Nyström理论用于减法聚类未采样样本之间密度权值矩阵的逼近,从而实现了对所有样本的密度值逼近,最后沿用经典减法聚类修正样本密度值的方法,实现整个减法聚类过程。将本文算法在人工数据、标准彩色图像及UCI数据集上进行了实验,详细说明了本文算法利用少数采样样本逼近多数未采样样本密度权值、密度值以及进行减法聚类的详细过程,并给出了聚类准确率、耗时及算法性能加速比。实验结果表明,与经典的减法聚类相比,本文算法在不影响聚类结果的情况下,对于较大规模数据集,可显著降低减法聚类的时间复杂度,极大程度地提高减法聚类的实时性能。     

基于改进模糊聚类与ANFIS的高速公路事件检测

为了准确并及时地发现高速公路上的交通事故隐患,减少事故引发的交通延迟,提高高速公路运行安全性,结合减法聚类与模糊C均值（FCM）聚类算法对输入样本数据进行聚类,建成初始模糊推理系统,然后通过神经网络的自学习机制,训练模糊系统参数,确定模糊推理规则,建立最终模糊模型。通过仿真实验结果对比,验证了基于改进模糊聚类与自适应神经模糊推理系统（ANFIS）建模方法的有效性。     

基于T S模型的模糊系统辨识方法综述*

模糊模型设计方法归结为两种,即语义驱动和数据驱动。数据驱动模型具有更好的性能,是目前研究的热点。模糊系统辨识是数据驱动下模糊系统建模的重要手段,辨识的优良直接影响系统建模的精度。模糊系统辨识可以分为两部分进行认识,即模糊系统结构辨识和参数辨识。回顾了近年来模糊系统辨识的理论和方法,如subtractive聚类、多分辨率自适应空间分解、SVM、核函数法、粒子群算法和并行遗传算法等。对各种算法原理、特点进行了介绍,对模糊系统辨识的发展进行了展望。     

基于T S模型的模糊系统辨识方法综述*

模糊模型设计方法归结为两种,即语义驱动和数据驱动。数据驱动模型具有更好的性能,是目前研究的热点。模糊系统辨识是数据驱动下模糊系统建模的重要手段,辨识的优良直接影响系统建模的精度。模糊系统辨识可以分为两部分进行认识,即模糊系统结构辨识和参数辨识。回顾了近年来模糊系统辨识的理论和方法,如subtractive聚类、多分辨率自适应空间分解、SVM、核函数法、粒子群算法和并行遗传算法等。对各种算法原理、特点进行了介绍,对模糊系统辨识的发展进行了展望。     

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.     

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson–Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.