Hybrid learning-based neuro-fuzzy inference system: a new approach for system modeling

In this article, a hybrid learning neuro-fuzzy inference system (HLNFIS) with a new inference mechanism is proposed for system modeling. In the HLNFIS, the incoming signal is fuzzified by the proposed improved Gaussian membership function (IGMF), which is derived from two standard Gaussian functions. With the premise construction with IGMFs, the system inference ability can be upgraded. The fuzzy inference processor, which involves both numerical and linguistic reasoning, is introduced in rule base construction. For effective parameter learning, the hybrid algorithm of random optimization (RO) and least square estimation (LSE) is exploited, where the premise and the consequence parameters of are updated by RO and LSE, respectively. To validate the feasibility and the potential of the proposed approach, three examples of system modeling are conducted. Through experimental results and comparisons the proposed HLNFIS shows excellent performance for complex modeling.     

Time series forecasting with a neuro-fuzzy modeling scheme

Time series forecasting concerns the prediction of future values based on the observations previously taken at equally spaced time points. Statistical methods have been extensively applied in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose an approach based on neuro-fuzzy modeling for time series prediction. Given a predicting sequence, the local context of the sequence is located in the series of the observed data. Proper lags of relevant variables are selected and training patterns are extracted. Based on the extracted training patterns, a set of TSK fuzzy rules are constructed and the parameters involved in the rules are refined by a hybrid learning algorithm. The refined fuzzy rules are then used for prediction. Our approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate prediction. It also works for one-step as well as multi-step prediction. Several experiments are conducted to demonstrate the effectiveness of the proposed approach.     

Prediction of Particlulate Air Pollution using Neural Techniques

We have analysed the possibility of predicting hourly average concentrations of suspended atmospheric particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5) several hours in advance using data obtained in downtown Santiago, Chile. By performing some standard tests used in the study of dynamical systems, we are able to extract some features of the time series of data. We use this information to estimate the amount of data on the past to be used as input to a neural network in order to predict future values of PM2.5 concentrations. We show that improvement of predictions is possible by using another neural network for noise reduction on the original series. The best results are obtained with a type of neural network which is equivalent to a linear regression. Up to six hours in advance, predictions generated in this way have significantly smaller errors than predictions based on the persistence of the long term average of the data.     

基于BP人工神经网络的雾霾天气预测研究

在雾霾天气准确预测的研究中,为更好的反映长春市空气质量状况,对长春市的PM2.5含量实测数据与同期的气象资料进行了分析.为科学准确地预测雾霾天气,根据BP人工神经网络可以逼近任意非线性函数的特点,提出了采用BP人工神经网络的雾霾天气预测系统.以长春市的PM2.5实时监测数据为时间序列数据样本,应用C++语言进行编程实现.最后建立了基于时间序列的BP神经网络雾霾天气预测模型.仿真结果表明,该模型能够较准确的预测雾霾天气.提高了预测的有效性和实用性.     

粒子群算法优化的BP网络预测软件质量

预测软件质量的技术中,软件建模技术是软件质量评价体系中的关键技术,它可以发现软件中度量数据和软件质量要素之间的非线性关系。BP神经网络能够很好地模拟度量数据和质量要素之间的非线性关系,但是BP网络存在易于陷入局部极小和收敛速度慢的问题,所以提出了用粒子群算法优化BP神经网络,通过优化的BP网络建立软件质量模型,这样能很好地解决BP网络收敛速度慢和局部极小的问题。在实现该进化BP神经网络的基础上,利用28组数据进行实验,并通过与BP模型的结果的比较,验证了该模型。     

The modeling of time series based on fuzzy information granules

A lot of research has resulted in many time series models with high precision forecasting realized at the numerical level. However, in the real world, higher numerical precision may not be necessary for the perception, reasoning and decision-making of human. Model of time series with an ability of humans to perceive and process abstract entities (rather than numeric entities) is more adaptable for some problems of decision-making. With this regard, information granules and granular computing play a primordial role. Fox example, if change range (intervals) of stock prices for a certain period in the future is regarded as information granule, constructing model that can forecast change ranges (intervals) of stock prices for a period in the future is better able to help stock investors make reasonable decisions in comparison with those based upon specific forecasting numerical value of stock price. In this paper, we propose a new modeling approach to realize interval prediction, in which the idea of information granules and granular computing is integrated with the classical Chen’s method. The proposed method is to segment an original numeric time series into a collection of time windows first, and then build fuzzy granules expressed as a certain fuzzy set over each time windows by exploiting the principle of justifiable granularity. Finally, fuzzy granular model can be constructed by mining fuzzy logical relationships of adjacent granules. The constructed model can carry out interval prediction by degranulation operation. Two benchmark time series are used to validate the feasibility and effectiveness of the proposed approach. The obtained results demonstrate the effectiveness of the approach. Besides, for modeling and prediction of large-scale time series, the proposed approach exhibit a clear advantage of reducing computation overhead of modeling and simplifying forecasting.     

基于片段模式的多时间序列关联分析

本文对基于片断模式的多时间序列关联分析进行了研究,提出了一种分析方法。这一方法是,首先通过聚类找出在时间序列中频繁出现的片断模式,然后将找到的片断模式作为模板,对时间序列进行跨事务关联分析。我们采用中国证券市场1997～2001年的数据为测试数据集,对我们提出的算法进行了测试。测试结果表明,我们的算法是有效的。     

A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.     

A probabilistic fuzzy approach to modeling nonlinear systems

Recently, the study of incorporating probability theory and fuzzy logic has received much interest. To endow the traditional fuzzy rule-based systems (FRBs) with probabilistic features to handle randomness, this paper presents a probabilistic fuzzy neural network (ProFNN) by introducing the probability of input linguistic terms and providing linguistic meaning into the connectionist architecture. ProFNN integrates the probabilistic information of fuzzy rules into the antecedent parts and quantifies the impacts of the rules on the consequent parts using mutual subsethood, which work in conjunction with volume defuzzification in a gradient descent learning frame work. Despite the increase in the number of parameters, ProFNN provides a promising solution to deal with randomness and fuzziness in a single frame. To evaluate the performance and applicability of the proposed approach, ProFNN is carried out on various benchmarking problems and compared with other existing models with a performance better than most of them.     

Modelling air pollution time-series by using wavelet functions and genetic algorithms

This paper proposes a novel approach based on the use of wavelet functions to model air pollution time series. One peculiarity of the approach is that of combining the use of wavelets and genetic algorithms to search for the best wavelet parameters. A case study, referring to the modelling of daily averages of SO₂ time series recorded in the industrial area of Syracuse (Italy) is reported in order to compare the performance of a wavelet-based prediction model and a Multi-layer perceptron neural model. The results obtained show that there are no significant differences between the neural and the wavelet approach in terms of model performance and computational effort. There is however an appreciable advantage in using the proposed wavelet-based technique in terms of model readability.The paper has been financially supported by the EU in the framework of the APPETISE project (Contract N. IST-99–11764). The author is also grateful to the Municipal and Provincial authorities in Syracuse (Italy) for providing the pollution and meteorological data considered in the paper. Finally the author is grateful to Dr Libero Bertucco who helped to code part of the software package considered in this work.     

时间序列模糊关联规则的挖掘

对于许多复杂系统产生的时间序列,研究序列的局部行为和局部关联特征往往比原来的研究系统全局性模型具有明显的优势。为研究时间序列内部或时间序列间局部形态的关联特征,文章借助模糊集来软化时间序列属性论域的划分边界从而研究时间序列局部形态的模糊关联规则、规则可信度和规则的评价方法。实际算例显示了算法的有效性。     

An accuracy-oriented self-splitting fuzzy classifier with support vector learning in high-order expanded consequent space

This paper proposes a self-splitting fuzzy classifier with support vector learning in expanded high-order consequent space (SFC-SVHC) for classification accuracy improvement. The SFC-SVHC expands the rule-mapped consequent space of a first-order Takagi-Sugeno (TS)-type fuzzy system by including high-order terms to enhance the rule discrimination capability. A novel structure and parameter learning approach is proposed to construct the SFC-SVHC. For structure learning, a variance-based self-splitting clustering (VSSC) algorithm is used to determine distributions of the fuzzy sets in the input space. There are no rules in the SFC-SVHC initially. The VSSC algorithm generates a new cluster by splitting an existing cluster into two according to a predefined cluster-variance criterion. The SFC-SVHC uses trigonometric functions to expand the rule-mapped first-order consequent space to a higher-dimensional space. For parameter optimization in the expanded rule-mapped consequent space, a support vector machine is employed to endow the SFC-SVHC with high generalization ability. Experimental results on several classification benchmark problems show that the SFC-SVHC achieves good classification results with a small number of rules. Comparisons with different classifiers demonstrate the superiority of the SFC-SVHC in classification accuracy.     

SOM time series clustering and prediction with recurrent neural networks

Local models for regression have been the focus of a great deal of attention in the recent years. They have been proven to be more efficient than global models especially when dealing with chaotic time series. Many models have been proposed to cluster time series and they have been combined with several predictors. This paper presents an extension for recurrent neural networks applied to local models and a discussion about the obtained results.     

Recursive prediction for long term time series forecasting using advanced models

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a time (t+h) using previous time steps (t-τ₁),(t-τ₂),…,(t-τ_n). Nevertheless, learning a model for long term time series prediction might be seen as a more complicated task, since it might use its own outputs as inputs for long term prediction (recursive prediction). This paper presents the utility of two different methodologies, the TaSe fuzzy TSK model and the least-squares SVMs, to solve the problem of long term time series prediction using recursive prediction. This work also introduces some techniques that upgrade the performance of those advanced one-step-ahead models (and in general of any one-step-ahead model), where they are used recursively for long term time series prediction.     

一种多知识融合的获取模糊规则的集成方法

基于单一知识发现方法的不足,提出了一种获取模糊规则的集成方法。首先用Kohonen网络进行数据量化,然后运用粗集理论产生初始规则,并根据所得的规则建立模糊神经网络模型,优化模糊规则的参数,最后再进一步简化获取模糊规则。通过实例进行系统仿真,结果表明该方法是有效的, 同时为获取模糊规则提供了新的思路。最后与其它方法进行了比较,并总结了该方法的特点。     

Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM–MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM–MSapriori model predicted the future values effectively and outperformed the FCM–Apriori model and other models reported in the literature.     

An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction

The prediction of time series has both the theoretical value and practical significance in reality. However, since the high nonlinear and noises in the time series, it is still an open problem to tackle with the uncertainties and fuzziness in the forecasting process. In this article, an evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems. The eRIT2IFNN employs interval type-2 intuitionistic fuzzy sets to enhance the modeling of uncertainties by intuitionistic evaluation and noise tolerance of the system. In the eRIT2IFNN, the antecedent part of each fuzzy rule is defined using intuitionistic interval type-2 fuzzy sets, and the consequent realizes the Takagi–Sugeno–Kang type fuzzy inference mechanism. In order to utilize the prior knowledge including intuitionistic information, a local internal feedback is established by feeding the rule firing strength of each rule to itself eRIT2IFNN is fully adaptive to the evolving of sequence data by online learning of structure and parameters. A modified density-based clustering is implemented for the structure learning, where both densities and membership degrees are involved to determine the fuzzy rules. Performance of eRIT2IFNN is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems. Moreover, the eRIT2IFNN is tested for identification of dynamics under both noise-free and noisy environments. Finally, a group of practical financial price-tracking problems including high-frequency data of financial future, commodity future and precious metal are used for the evaluation of the proposed inference system.     

Intelligent design and planning of manual assembly workstations: A neuro-fuzzy approach

The design of manual assembly workstations, as with most forms of designs, is highly iterative and interactive. The designer has to consider countless constraints and solutions for contradictory goals. In order to assist the designer in design process, it is required to develop a new intelligent methodology and system. This paper develops a neuro-fuzzy hybrid approach to intelligent design and planning of manual assembly workstations. Problems, related to workstation layout design, planning, and evaluation, are discussed in detail. A fuzzy neural network is used to predict the ranges of anatomical joint motions and to design or adjust workstations and tasks. The neuro-fuzzy computing scheme is integrated with operator's posture analysis and evaluation. For training and test purposes, experiment is carried out to simulate assembly tasks on a multi-adjustable assembly workstation equipped with a flexible PEAK motion measurement and analysis system. The trained neural network is capable of memorizing and predicting the joint angles associated with a range of workstation configurations. Thus, it can also be used for the design/layout and on-line adjustment of manual assembly workstations. Thus, the developed system provides a unified, computational intelligent framework for the design, planning and simulation of manual assembly workstations.     

Statistical fuzzy interval neural networks for currency exchange rate time series prediction

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.     

Determining optimal quality distribution of latex weight using adaptive neuro-fuzzy modeling and control systems

This paper introduces a systematic approach for the design of an adaptive neuro-fuzzy inference system (ANFIS) for latex weight control of level loop carpets. In high production volume of some industries, manual control could lead to undesirable variations in product quality. Therefore, process parameters require continuous checking and testing against quality standards. One way to overcome this problem is to use statistical process control by which a complete elimination of variability may not be possible. Fuzzy logic (FL) control is one of the most significant applications of fuzzy logic and fuzzy set theory. Fuzzy if-then rules (controllers) were developed in a systematic way that formed the backbone of the neuro-fuzzy control system. The developed ANFIS was able to produce crisp numerical outcomes to predict latex weights. The neuro-fuzzy system behaved like human operators. ANFIS outcomes were encouraging because they provide a more efficient and uniform distribution of latex weight and seemed to be better than the other statistical process control tools. FL controllers provide a feasible alternative to capture approximate, qualitative aspects of human reasoning and decision making processes.