Fuzzy Prediction Model Research Articles

A Collaborative Multiplicative Holt-Winters Forecasting Approach with Dynamic Fuzzy-Level Component

The adoption of forecasting approaches such as the multiplicative Holt-Winters (MHW) model is preferred in business, especially for the prediction of future events having seasonal and other causal variations. However, in the MHW model the initial values of the time-series parameters and smoothing constants are incorporated by a recursion process to estimate and update the level (LT), growth rate (bT) and seasonal component (SNT). The current practice of integrating and/or determining the initial value of LT is a stationary process, as it restricts the scope of adjustment with the progression of time and, thereby, the forecasting accuracy is compromised, while the periodic updating of LT is avoided, presumably due to the computational complexity. To overcome this obstacle, a fuzzy logic-based prediction model is developed to evaluate LT dynamically and to embed its value into the conventional MHW approach. The developed model is implemented in the MATLAB Fuzzy Logic Toolbox along with an optimal smoothing constant-seeking program. The new model, proposed as a collaborative approach, is tested with real-life data gathered from a local manufacturer and also for two industrial cases extracted from literature. In all cases, a significant improvement in forecasting accuracy is achieved.

Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system

Remaining useful life (RUL) prediction of machining tools is a typical multi-sensor information fusion problem. It involves the use of the monitoring information acquired from different types of sensors installed on computer numerical control machine to realize the RUL prediction of the machining tools in cutting process. Owing to the nonlinear and stochastic nature between the extracted features and tool wear level, the promptness and precision of online RUL prediction of machining tools are still difficult to be obtained. In this paper, a multi-sensor information fusion system for online RUL prediction of machining tools is proposed. The system includes sensor signal preprocessing based on ensemble empirical mode decomposition method, statistics feature extraction based on time domain and frequency domain analysis, optimum feature selection based on Pearson correlation coefficient, monotonicity and autocorrelation, feature fusion based on adaptive network based fuzzy inference system and RUL prediction model based on polynomial curve fitting method. We report a practical application of this multi-sensor information system and estimate its prediction performance. The proposed system may be applied to the industrial field. Meanwhile, the comparison between the proposed method and other standard methods is carried out using several statistical indices.

Mamdani-Fuzzy Modeling Approach for Quality Prediction of Non-Linear Laser Lathing Process

Lathing is a process to fashioning stock materials into desired cylindrical shapes which usually performed by traditional lathe machine. But, the recent rapid advancements in engineering materials and precision demand gives a great challenge to the traditional method. The main drawback of conventional lathe is its mechanical contact which brings to the undesirable tool wear, heat affected zone, finishing, and dimensional accuracy especially taper quality in machining of stock with high length to diameter ratio. Therefore, a novel approach has been devised to investigate in transforming a 2D flatbed CO2 laser cutting machine into 3D laser lathing capability as an alternative solution. Three significant design parameters were selected for this experiment, namely cutting speed, spinning speed, and depth of cut. Total of 24 experiments were performed with eight (8) sequential runs where they were then replicated three (3) times. The experimental results were then used to establish Mamdani - Fuzzy predictive model where it yields the accuracy of more than 95%. Thus, the proposed Mamdani - Fuzzy modelling approach is found very much suitable and practical for quality prediction of non-linear laser lathing process for cylindrical stocks of 10mm diameter.

Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process.

Prediction of whiteness index of cotton using bleaching process variables by fuzzy inference system

A fuzzy prediction model has been built based on hydrogen peroxide concentration, temperature and time of bleaching as the input variables and knitted fabric whiteness index as the output variable. The process parameters affecting the whiteness index of cotton knitted fabrics are very non-linear. Fuzzy inference system is a prospective modeling tool as it can map effectively in nonlinear domain with minimum investigational data. Triangular-shaped membership functions were considered for the variables and total 48 rules were created in this study. It was found that the sole effect of the concentration of hydrogen peroxide on whiteness is pretty low, but is affected by temperature noticeably even in a fixed concentration of hydrogen peroxide. The model proposed in the present study has been verified by additional experimental data set. The root mean square, mean absolute error percentage and coefficient of determination (R2) between the predicted and experimental values were found to be 0.536, 0.798 and 0.959 respectively. The results validate that the model can be applied suitably for the prediction of fabric whiteness index in textile industries.

Quality Prediction Of Friction Stir Weld Joints On AA 5052 H32 Aluminium Alloy Using Fuzzy Logic Technique

This paper addresses the development of fuzzy model for weld quality prediction. Significant friction stir welding parameters affecting the weld quality are tool pin geometry, tool rotational speed, welding speed, and tool tilt angle. Welding experiment is performed on AA 5052-H32 aluminium plates by central composite design to attain maximum tensile strength of the weld joint. Quality of weld measured in terms of tensile strength and percentage elongation is predicted using fuzzy logic and the results are compared with statistical analysis. Confirmatory experimental results show that the fuzzy model can predict an adequate output with less error than statistical analysis.

Ensemble of evolving data clouds and fuzzy models for weather time series prediction

This paper describes a variation of data cloud-based intelligent method known as typicality-and-eccentricity-based method for data analysis (TEDA). The objective is to develop data-centric nonlinear and time-varying models to predict mean monthly temperature. TEDA is an incremental algorithm that considers the data density and scattering of clouds over the data space. The method does not require a priori knowledge of the dataset and user-defined parameters. However, if some knowledge about the number of clouds and rules is available, then it can be expressed through a single parameter. Past values of minimum, maximum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity are considered in the analysis. A non-parametric Spearman correlation based method is proposed to rank and select the most relevant features and time delays for a more accurate prediction. The datasets were obtained from weather stations located in main Brazilian cities such as Sao Paulo, Manaus, Porto Alegre, and Natal. These cities are known to have particular weather characteristics. TEDA results are compared with results provided by the evolving Takagi–Sugeno (eTS) and the extended Takagi–Sugeno (xTS) methods. Additionally, an ensemble of cloud and fuzzy models and fuzzy aggregation operators is developed to give single-valued and granular predictions of the time series. Granular predictions convey a range of possible temperature values and give an idea about the error and uncertainty associated with the data.

A fuzzy KNN-based model for significant wave height prediction in large lakes

Summary Some algorithms based on fuzzy set theory (FST) such as fuzzy inference system (FIS) and adaptive-network-based fuzzy inference system (ANFIS) have been successfully applied to significant wave height (SWH) prediction. In this paper, perhaps for the first time, the fuzzy K-nearest neighbor (FKNN) algorithm is utilized to develop a fuzzy wave height prediction model for large lakes, where the fetch length depends on the wind direction. As fetch length (or wind direction) can affect the wave height in lakes, this variable is also considered as one of the inputs of the prediction model. The results of the FKNN model are compared with those of some soft computing techniques such as Bayesian networks (BNs), regression tree induction (named M5P), and support vector regression (SVR). The developed FKNN model is used for SWH prediction in the western part of Lake Superior in North America. The results show that the FKNN and M5P model can outperform the other soft computing techniques.

A fuzzy logic model for prediction of tensile properties of epoxy/glass fiber/silica nanocomposites

The object of this work is to study and predict the tensile properties (tensile strength, elastic modulus, and elongation at break) of ternary nanocomposites based on epoxy/glass fiber/nanosilica using the fuzzy logic (FL). Two factors in three levels including glass fiber at 0, 5, and 10 wt% and nanosilica at 0, 0.5, and 1 wt% were chosen for adding to an epoxy matrix. From FL surfaces, it was found that the glass fiber content had a main role in the tensile properties of nanocomposites. The high levels of glass fiber content led to a significant increase in the elastic modulus and generally, the presence of glass fiber decreased the tensile strength and elongation at break. Also, addition of the nanosilica content resulted in an increased elastic modulus but decreased the elongation at break of nanocomposites. Finally, an FL model was obtained for each tensile property.

Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

In this paper, we have developed an interval type-2 fuzzy logic controller (T2FLC) approach for assessment of the risks that workers expose to at construction sites. Using this novel approach, past accident data, subjective judgments of experts, and the current safety level of a construction site are to be combined. The method is then implemented on a tunneling construction site and risk level for all type of accidents is formulated. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of risk assessment can be predicted in industry. Finally, a comparative study has been successfully performed with type-1 and type-2 fuzzy dataset for improving risk assessment that can be easily determined in the type-2 fuzzy prediction model for improving accuracy. Validity of the proposed model is done with the help of statistical analysis and multiple linear regressions.

A short-term traffic prediction model in the vehicular cyber–physical systems

The advances in Cyber–Physical Systems (CPS), vehicular networks and Intelligent Transportation System (ITS) boost a growing interest in the design, development and deployment of Vehicular Cyber–Physical Systems (VCPS) for some emerging applications. As one of the key application for realizing traffic guidance, the traffic prediction could provide better route planning for people and accuracy decision basis for traffic managements. In practice, short-term traffic information has the characteristics of real-time, incompleteness, non-linearity and non-stationary, and few proposed methods could successfully implement this forecasting. In this paper, we proposed a fuzzy Markov prediction model which can estimate the short-term traffic conditions in VCPS in urban environment. First, we selected a real-time GPS dataset in the Shanghai Transport Grid Project as our data source for traffic prediction and pre-process this raw dataset to make it consistent with the practical case. Next, we combine the fuzzy theory with Markov progress in the prediction model, and use the continuous three-step average method to reduce the errors caused by the one-step transition. Finally, we choose the speed and traffic flow to express the metrics of traffic state and use the fuzzy reasoning rules to give out the determined traffic state. The simulation results show that our proposed model can be precisely used for the short-term traffic prediction in urban environment.

A new approach of GA-based type reduction of interval type-2 fuzzy model for nonlinear MIMO system: Application in methane oxidation process

The aim of this paper is the application of a type-2 fuzzy model to a nonlinear MIMO system. In the suggested approach, genetic algorithm is employed to find the optimal type reduction of the interval type-2 fuzzy model for model prediction. To verify its performance, the proposed method is used in prediction of the methane conversion and the formic acid selectivity of the methane oxidation process with H2O2. In this process, the input variables are temperature, pressure, time on stream, catalyst content, H2O2 concentration and stirring speed. Interval type-2 fuzzy logic model has designed with reduced rules number compared with the type-1 fuzzy model. The results confirm that the proposed method based on type reduction interval type-2 fuzzy model outperforms the other strategies with an R2 value of greater than 0.988.

A distributed model for urban water demand prediction

The prediction of urban water demand is likely to play a significant role in the planning of urban water supply and drainage pipe network. The traditional water demand forecasting methods, including the water index method and the fuzzy water demand prediction model, are widely used with some deficiencies. The water demand prediction results varied among different water quantity indexes with subjectivity. The prediction results by fuzzy water demand prediction model, with fuzzy model structure, are lack of spatial distribution. Meanwhile, the process of urban water demand is closely related to the land use of urban unit. The difference of intensity and characteristic of water demand is obvious among different land use types. In this paper, a distributed urban water demand prediction model was established based on the relationship of water demand process, considering of the eight types of urban construction land use data and three types of major water demand data. In this model, we assumed the linear relationship between water demand and land use unit, ignored the fire water demand because with more randomness than other water demand process. The distributed model for urban water demand prediction was applied in Xiamen. We obtained the land use data and water demand data in 2004–2014 of Xiamen from official statistics. The monolayer construction area and the number of building stories in the residential units obtained by the high definition satellite image and the actual measurement were used to calculated the area of residential land unit, while the area of other land unit was obtained from official land planning in 2020 of Xiamen. The water demand parameters of every urban construction land unit were calibrated by PEST, and the rationality of parameters was analyzed. We observed the stability of the water demand parameters in the model while the results showed great similarity with different calibration period in the same validation period. The water demand of Xiamen and its spatial distribution in 2020 were predicted by the distributed water demand prediction model. The results showed that the water demand of Xiamen in 2020 will reach to 366.57 million tons, which will increase by 24.2% than that in 2014; the water demand intensity of residential land and industrial land will be greater than other types of construction land; the water demand intensity of residential land in Xiamen Island will be greater than the other residential land. The simulation results proved that the spatial distribution of water demand has positive correlation with population density in urban area. We observed the great difference of water demand intensity among different types land use with great difference in the process of urban water demand. The results also showed the excellent performance of the distributed model for urban water demand prediction both in calibration and validation when applied in Xiamen urban water demand prediction. Compared with the traditional urban water demand prediction model, the distributed water demand prediction model, with bright application prospect, can obtain the distributed urban water demand data with relatively stable parameters which can be determined by water demand data and land use data in the past years.

Takagi–Sugeno fuzzy generalized predictive control for a class of nonlinear systems

In this paper, a fuzzy generalized predictive control method for a class of nonlinear systems is studied. Firstly, based on the Takagi–Sugeno fuzzy model, a class of nonlinear systems and its fuzzy predictive model are presented. Next, based on the controlled autoregressive integrating moving average model transformed by the Takagi–Sugeno fuzzy model of the nonlinear systems, a novel fuzzy generalized predictive control method is proposed for the nonlinear systems, which combines fuzzy techniques and generalized predictive control theory. Lastly, two typical examples, the three-dimensional Lorenz nonlinear system and the four-dimensional Chen nonlinear system, are employed to verify the effectiveness and superiority of the proposed scheme. It also provides a reference for relevant control of nonlinear even chaotic systems.

A fuzzy logic-based prediction model for fracture force using low-power fiber laser beam welding

Laser beam welding is an advanced technique to join dissimilar materials together. Important laser joining parameters such as laser power, welding speed, and overlapping factor would determine the weld penetration of the joints. In the current study, a single-sided welding of a titanium alloy, Ti6Al4V, and a nickel-based alloy, Inconel 600, in a T-joint configuration was conducted using a continuous-wave, low-power fiber laser. The strengths of the welded joints were evaluated using pull tests. These results were used to build an intelligent fuzzy expert system model to predict the fracture force of the joint. Using MATLAB R2009b, the fuzzy logic development was made based on the Mamdani technique. Twenty-four real-time experiments were carried out and 18 numerical testing data were used to develop the fuzzy logic. The resulting viewer surfaces showed that the overlapping factor was the highest influencing laser welding parameter, followed by the welding speed and the laser power. The calculated relative error between the real and predicted results was 6.95% indicating acceptable results. This was supported by the goodness of fit value of 0.9849. The findings of this study have extended the knowledge of the fracture force prediction using the fuzzy expert system model.

Artificial intelligence modelling and analysis of pivotal parameters in drilling hybrid fiber composite (HFC)

Composite materials have the perk of combining a number of properties, which are not usually found together in a single material. The Hybrid Fiber composite solves the increasing need for Eco friendly materials coupled with high durability and strength. HFC has a wide range of applications in the field of aerospace, auto motives, civil constructions and electronics at a cost, which is lower than that of the traditional fibers like GFRP. Drilling is one of the primary operations adopted by industries for material working and component building. In this paper, the seminal process parameters - spindle speed, feed rate and the drill diameter are analysed and a Fuzzy model is burgeoned predicting the Thrust force and Torque for drilling Hybrid fiber Composite (HFC). The results indicate that the Fuzzy prediction model is effective and facilitates a better and easier artificial intelligence modelling.

Fuzzy logic model for prediction of cold filter plugging point of biodiesel from various feedstock

The present study investigates the potential of Fuzzy Logic model to predict the cold filter plugging point (CFPP) of biodiesel fuel from its fatty acid (FA) composition. CFPP defines the operability for diesel fuel which is strongly influenced by the FA of feedstock. Prediction of CFPP based on the FA of feedstock can reduce the experimental effort to produce a biodiesel suitable for a regional climate. To generate fuzzy prediction model, data sets containing FA composition and CFPP temperatures of 60 biodiesel samples traced from literature. To check the accuracy of the model developed, waste/refined frying oil (WFO/RFO) and waste/refined canola oil (RCO/WCO) were converted to biodiesel fuel by transesterification then, their CFPP temperatures were determined following the EN and ASTM standards. The CFPP temperatures estimated by the multi input single output (MISO) fuzzy model were in close agreement with the experimental values. The validation results confirm the applicability of the fuzzy model developed here with its high degree of accuracy and minimum time demand can be rapid alternative to the costly and time consuming actual experiments and trails.

A fuzzy transition based approach for fault severity prediction in helical gearboxes

Rotating machinery is an important device supporting manufacturing processes, and a wide research works are devoted to detecting and diagnosing faults in such machinery. Recently, prognosis and health management in rotating machinery have received high attention as a research area, and some advances in this field are focused on fault severity assessment and its prediction. This paper applies a fuzzy transition based model for predicting fault severity conditions in helical gears. The approach combines Mamdani models and hierarchical clustering to estimate the membership degrees to fault severity levels of samples extracted from historical vibration signals. These membership degrees are used to estimate the weighted fuzzy transitions for modelling the evolution along the fault severity states over time, according to certain degradation path. The obtained fuzzy model is able of predicting the one step-ahead membership degrees to the severity levels of the failure mode under study, by using the current and the previous membership degrees to the severity levels of two available successive input samples. This fuzzy predictive model was validated by using real data obtained from a test bed with different damages of tooth breaking in the helical gears. Results show adequate predictions for two scenarios of fault degradation paths.

Fuzzy multivariate NARX model for passenger entrance flow prediction in the Shanghai subway system

Fuzzy multivariate NARX model for passenger entrance flow prediction in the Shanghai subway system

Machining analysis of natural fibre reinforced composites using fuzzy logic

In this work, a new composite plate with natural jute fibre as the reinforcement fibres and isophthalic polyester as the resin was manufactured and subjected to a series of end milling operation by changing three input factors namely speed, feed rate and depth of cut. During each operation, the output responses namely thrust force and torque were measured. The responses were analyzed using Taguchi method to examine the relation between the input factors and output responses, and also to know the most influencing factors on the responses. The data was also analyzed using fuzzy rule model for prediction of responses for a range of input factors. The results showed that all three factors chosen have significant effect on the responses. The fuzzy model data in comparison with the experimental values shows only a marginal error and hence the prediction was highly satisfactory.