Fuzzy Prediction Research Articles

Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with genetic algorithms (FLSSVRGA) to forecast seasonal revenues. The FLSSVRGA uses the H-level to control the possibility distribution range yielded by the fuzzy model and to provide the fuzzy prediction interval. Depending on various factors, such as the global economy and government policies, a decision maker can elect a different level for H using the FLSSVRGA. The proposed FLSSVRGA model is a rolling forecasting model with time series data updated monthly that predicts revenue for the coming month. Four other forecasting models: the seasonal autoregressive integrated moving average (SARIMA), generalized regression neural networks (GRNN), support vector regression with genetic algorithms (SVRGA) and least-squares support vector regression with genetic algorithms (LSSVRGA), are employed to forecast the same data sets. The experimental results indicate that the FLSSVRGA model outperforms all four models in terms of forecasting accuracy. Thus, the FLSSVRGA model is a useful alternative for forecasting seasonal time series data in an uncertain environment; it can provide a user-defined fuzzy prediction interval for decision makers.

Geological controls on prediction of coalbed methane of No. 3 coal seam in Southern Qinshui Basin, North China

article i nfo In order to better understand the geological controls on coalbed methane (CBM) in Southern Qinshui basin (SQB), North China, geological surveys and laboratory experiments, including coal petrology analysis, proximate analysis and methane adsorption/desorption, were conducted. Results show that the coals from the SQB contain 0.59-3.54% moisture, 3.5-15.54% ash yield, 73.62-88.92% fixed carbon and 2.14-4.04% hydrogen, with C/H ratios in the range of 19.96-36.25. The vitrinite reflectance (Ro,m) ranges from 1.95 to 3.49%. The coals are composed of 18.5-97.4% vitrinite and 2.4-81.4% inertinite. The geologic structures, coal-bearing strata and coal depositional environment were studied by both field geological research and laboratory tests. A positive relationship is found between CBM content and basin hydrodynamics, in which CBM easily concentrates in the groundwater stagnant zone because of the water pressure. Furthermore, integrated geographical information system (GIS) and analytical hierarchy fuzzy prediction method (AHP) were used to evaluate the CBM resources in the SQB. The results show that the amount of CBM associated with the No. 3 coal seam in the SQB is 3.62×10 11 m 3 .T he CBM resource concentration (gas-in-place per square kilometer) in the SQB is in the range of (0.72- 2.88)×10 8 m 3 /km 2 , with an average of 1.21×10 8

Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques

The current form of Web provides numerous product resources available to users. Users can rely on intelligent agents for purchase actions. These actions are taken in specific environments such as Electronic Markets (EMs). In this paper, we study the interaction process between buyers and sellers and focus on the buyer side. Each buyer has the opportunity to interact with a number of sellers trying to buy the most appropriate products. This interaction can be modeled as a finite horizon Bargaining Game (BG). In this game, players have opposite goals concerning the product price. We adopt a number of techniques in the buyer side trying to give the appropriate level of efficiency in the buyer decision process. The buyer uses a prediction mechanism in combination with the use of Fuzzy Logic (FL) theory in order to be able to predict the upcoming seller proposal and, thus, understand the seller pricing policy. Based on this, he/she can adapt his/her behavior when trying to purchase products. The buyer adaptation mechanism produces the belief that the buyer has about the seller pricing policy and a parameter that indicates his/her own pricing policy which yields the buyer offers in the upcoming rounds. Moreover, the buyer is based on FL system that derives the appropriate actions at every round of the BG. Our results show that the combination of Fuzzy Logic (FL) with the above-mentioned techniques provides an efficient decision mechanism in the buyer side that in specific scenarios outperforms an optimal stopping model.

Residential Property Price Prediction with FNN Network Model

In order to predict the new residential property market price, a Fuzzy Neural Network (FNN) prediction model was proposed. It was used to estimate the appropriate price level for a new property by learning from historical data on the correlations between various factors that influence the prices of properties and the actual selling prices. In particular, an artificial neural network prediction model was developed to compare it accuracy with the fuzzy neural network prediction model. The experimental results show that the fuzzy neural network prediction model has strong function approximation ability and is suitable for residential properties price prediction depending on the quality of the available data.

Formation of a Real-Time Time Scale With Asia-Pacific TWSTFT Network Data

This paper presents a novel scheme to generate a real-time paper time scale by utilizing ensemble clock data and two-way satellite time and frequency transfer (TWSTFT) data of time laboratories in the Asia-Pacific region. Based on integration of the aforementioned data with a specific weighting method and a fuzzy prediction approach, the reference paper time scale devised in this paper can be generated in real time. The validity of the proposed scheme is investigated by undertaking about one year's worth of experiments, which are based on the calculated clock ensembles from TWSTFT links among Asian time laboratories. Moreover, the performance of the proposed scheme is evaluated with respect to the time scales of Coordinated Universal Time (UTC) and local realizations of UTC. The simulated real-time reference time scale based on the proposed scheme is within 10 ns with UTC, and time deviation is less than 0.6 ns for a time interval exceeding 60 days.

HCBKA 기반 IT2TSK 퍼지 예측시스템 설계

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

A Two-Stage GIS-Based Suitability Model for Siting Biomass-to-Biofuel Plants and its Application in West Virginia, USA

Woody biomass has been considered of low value because the cost of removal generally exceeded market price. New, valued-added markets to offset removal costs are necessary for utilization to be effective. In recent years the use of biomass as feedstock for biofuel production in the United States has been on the rise. A variety of liquid fuels can be produced from woody biomass; ethanol is one of the most promising. This study presents a two-stage approach to selecting woody biomass-based biofuel plants using Geographical Information System (GIS) spatial analysis and the multi-criteria analysis ranking algorithm of compromise programming. Site suitability was evaluated to minimize direct cost for investors and potential negative environmental impacts. The first step was to create a site suitability index using a linear fuzzy logic prediction model. The model involved 15 variables in three factor groups: (1) general physical conditions, (2) costs, and (3) environmental factors. The weights of the cost factors were determined using pairwise comparisons in the Analytical Hierarchy Process (AHP). The value of site suitability was reclassified into three categories (non-suitable, low-suitable, and high-suitable) using different classification methods. With a feasible plant location defined as an industrial site within the most suitable area, the second stage of the analysis used compromise programming to compare the potential sites. The criteria used to rank the potential sites included fuzzy distance to woody biomass, highways, railways, commercial airports, communities, and available parcel size. The AHP was used to compute the relative importance of each criterion. The top ten suitable sites were determined, and sensitivity analyses were conducted to derive the most preferred sites. The approach was successful in taking a large amount of non-commensurate spatial data and integrating a site-based ranking algorithm to find the top locations for biomass plants. It also has great potential and applicability to other suitability and site selection studies.

Research of fuzzy prediction algorithm in SOC estimation of new energy vehicles

Research of fuzzy prediction algorithm in SOC estimation of new energy vehicles

Fuzzy prediction of chaotic time series based on fuzzy clustering

The main purpose of this paper is to study a new method to model and predict a chaotic time series using a fuzzy model. First, the GK fuzzy clustering method is used to confirm the input space of the fuzzy model. The goal is to divide the training patterns into representative groups so that patterns within one cluster are more similar than those belonging to other clusters. Then, the Kalman filtering algorithm with singular value decomposition is applied to estimate the consequent parameters of the fuzzy model in order to avoid error delivery and error accumulation. The effectiveness of the proposed method is evaluated through simulated examples, including Mackey-Glass time series and Lorenz chaotic systems. The results show that the proposed method provides effective and accurate prediction. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

A fuzzy prediction model for calling communities

The analysis of logs related to social communities has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In general, identifying calling communities can be used to determine a particular customer's value according to the general pattern of behaviour of the community that the customer belongs to; this helps in creating an effective targeted marketing design, which is significantly important for increasing profitability. In the telecommunications industry, machine-learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behaviour such as churn prediction. In this paper, we pursue the identification of the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customer calling patterns are then input to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply two different classification methods: the Support Vector Machine (SVM) algorithm and the fuzzy genetic classifier. The latter method is used for possibly assigning a customer to different classes with different degrees of membership. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

Systems-Modelling of Compact Tension Energy in High Strength Pipeline Steel

AbstractThe research in this paper aims at linking the steel crack propagation process to the prediction of released flat fracture energy. In this work, an adaptive fuzzy modelling approach and a neural network model with double loop training process were developed in order to build the model for the prediction of flat fracture energy which is normally released during the compact tension (CT) test of X100 pipeline steel. Using the proposed modelling technique, a rule-based fuzzy prediction model as well as a Back-Propagation (BP) neural network model were constructed and optimized automatically from the experimental data. These two models related the load, crack mouth opening displacement (CMOD), and crack length to the released flat fracture energy. The relationship between the fracture propagation and the flat fracture energy was investigated in this paper in detail. The results showed that the elicited model is able to predict the flat fracture energy using the proposed modelling approach, and can hence form an important part of a more comprehensive modelling structure in the future.

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Abstract Antipollution legislation in automotive internal combustion engines requires active control and prediction of pollutant formation and emissions. Predictive emission models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization ( eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states.

Fuzzy time series prediction using hierarchical clustering algorithms

Abstract In many cases, the k-means clustering algorithm has been most frequently used to the field of data mining, fuzzy control systems and prediction since it was designed in simple procedures and excellent ability of classification. However, it sometimes brought about the failed results for non-linear data by classification behavior caused by just considering the statistical characteristics of non-linear data such as distances between data. To overcome the problems above, this paper proposes a new clustering algorithm of which the structure hierarchically classifies non-linear data. The proposed hierarchical classification technique consists of two levels, called upper clusters and lower fuzzy sets, using the cross-correlation clustering algorithm combined with the k-means clustering algorithm (HCKA), and it was able to improve classification accuracy. In addition, this paper constructs multiple model fuzzy predictors (MMFPs) corresponding to difference data of original time series, which was able to reflect the various characteristics of the time series to the proposed system. Simulation results show that the proposed system was effective and useful for modeling and predicting non-linear time series.

The FNN Quilting Process Deformation Prediction Model

It is easy to have deformation that non-rigid materials is on high-speed processing, so this paper introduces the fuzzy neural networks combine with computer vision measurement technology to control this process. Based on the traditional PID control, increasing a fuzzy neural network predictor for pre-processing of trajectory compensation. Established a fuzzy neural network deformation prediction model of the single needle quilting, and simulated. Experimental and simulation results show that: error compensation which based on fuzzy neural network, have a good real-time, allow fast and accurate automated processing of quilting.

Fuzzy Time Series Estimation and Prediction: Criticism, Suitable New Methods and Experimental Evidence

Fuzzy Time Series Estimation and Prediction: Criticism, Suitable New Methods and Experimental Evidence

A fuzzy prediction based trajectory estimation

Image processing, object tracking and trajectory estimation are the integral topics of computer vision which are rapidly gaining importance due to their non-ignorable relation with defense, securit...

Interval Type-2 TSK 퍼지논리시스템 기반 다중 퍼지 예측시스템 설계

본 논문은 예측 시스템의 성능을 개선하기 위해 비선형데이터의 내재된 특성이나 불확실성을 보다 효과적으로 반영할 수 있는 Interval Type-2 TSK 퍼지논리 시스템 기반 다중 퍼지 예측시스템의 설계를 다룬다. 본 논문에 제시된 다중 예측시스템들은 데이터의 비선형적 특성들을 효과적으로 고려하기 위해 설계되며, 각각의 시스템은 Type-1 TSK 퍼지논리나 다른 방법들에 비해 데이터의 불확실성을 충분히 반영할 수 있는 Interval Type-2 TSK 퍼지논리를 기반으로 구현된다. 또한, 1차 차분변환 과정을 통해, 데이터의 원형으로부터 최적의 차분데이터를 생성하고, 이들을 각 시스템의 입력으로 사용함으로써 시스템 설계 시 보다 안정된 통계적 정보를 제공할 수 있도록 한다. 마지막으로, 두 개의 전형적인 시계열 데이터의 예측 시뮬레이션을 통해 제안된 방법의 효용성을 검증한다. This paper presents multiple fuzzy prediction systems based on an Interval type-2 TSK fuzzy Logic System so that the uncertainty and the hidden characteristics of nonlinear data can be reflected more effectively to improve prediction quality. In proposed method, multiple fuzzy systems are adopted to handle the nonlinear characteristics of data, and each of multiple system is constructed by using interval type-2 TSK fuzzy logic because it can deal with the uncertainty and the characteristics of data better than type-1 TSK fuzzy logic and other methods. For input of each system, the first-order difference transformation method are used because the difference data generated from it can provide more stable statistical information to each system than the original data. Finally, computer simulations are performed to show the effectiveness of the proposed method for two typical time series examples.

Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction

System virtualization provides low-cost, flexible and powerful executing environment for virtualized data centers, which plays an important role in the infrastructure of Cloud computing. However, the virtualization also brings some challenges, particularly to the resource management and task scheduling. This paper proposes an efficient dynamic task scheduling scheme for virtualized data centers. Considering the availability and responsiveness performance, the general model of the task scheduling for virtual data centers is built and formulated as a two-objective optimization. A graceful fuzzy prediction method is given to model the uncertain workload and the vague availability of virtualized server nodes, by using the type-I and type-II fuzzy logic systems. An on-line dynamic task scheduling algorithm named SALAF is proposed and evaluated. Experimental results show that our algorithm can improve the total availability of the virtualized data center while providing good responsiveness performance.

Forecasting Thermal State in Blast Furnace Using Fuzzy Prediction Controller Optimized by Wavelet De-Noising

A new fuzzy prediction controller is established to prediction for silicon content in blast furnace hot metal. The forecasting process is only used the historical information of silicon content. This new algorithm consists of five steps: step 1 de-noises silicon content numerical data by wavelet analysis to smooth out noise; step 2 divides the input and output spaces of the de-noising numerical data into fuzzy regions; step 3 generates fuzzy rules from the de-noising data; step 4 assigns a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; step 5 determines a fuzzy prediction controller from input space to output space based on such rules. Simulation results show that: 84% hit rates of prediction in the range of [Si] 0.1% is attained using the prediction controller.