Fuzzy Prediction Research Articles

Fuzzy time series-based cross-layer optimization scheme for cooperative communication in wireless network

The cross-layered concept of wireless network breaks the traditional network layered concept to realize interaction between layers. In addition, the broadcasting nature of a wireless environment makes cooperative communications possible. In this paper, by utilizing the cross-layer optimization scheme, we introduce the fuzzy time series predication model that has linear computational complexity to forecast network performance. When we establish a fuzzy time series model, the sent queue length of the link layer is taken into consideration for the network layer's throughput prediction. In addition, we propose a fuzzy time series-based congestion control framework that is combined with a cooperative communication mechanism, and we apply the fuzzy time series prediction results in the preprocessing of network congestion. Through simulation, we demonstrate that the proposed cross-layer cooperative strategies achieve significant network throughput improvement and average packet delivery ratio.

Fuzzy Tier-based User Experience Prediction Scheme

uilding professional and efficient systems by using user experience became one of the important research activities that focus on the interactions between products, applications, designers, and users. Unfortunately, using user experience faces many problems. One of these problems is how to predict a user experience efficiently to build robust, effective, and flexible applications. To solve this problem, it is needed to design an optimal and efficient method for predicting user experience which includes behavior and emotions experiences. In this paper, a two-tier ranking scheme by using two multi-criteria decision making approaches is proposed. This proposed scheme considers a user experience as a sequence of executed actions or operations and it can predicate the most efficient user experience sequence of operations among a group of user experiences or experiences of individual users on a certain system or application. It uses the combination of two multi-criteria decision making approaches, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) in Fuzzy environments to rank each operation or action in a user sequence. Based on operation rank, in the first tier, the proposed algorithm selects all sequential operations with the highest ranks. If there are sub goals are not satisfied in the first tier, then in the second tier, the algorithm ranks all unselected operations and add all operations with the highest ranks which satisfy these sub goals. This new scheme is presented as a flexible and efficient method for predicting user experience which will be help designers and developers in building professional systems and applications. Keywordscomputer interaction, User experience design, Fuzzy sets, AHP, TOPSIS.

Overview of Fuzzy Logic Technique for Modeling Machining Process

This paper describes an overview of Fuzzy Logic (FL) application for solving machining problems. The developed fuzzy prediction model is an essential operational guideline for machinist in decision making and adjusting process parameters. This paper also discussed the previous literature that applied the FL in modeling machining process.

Fuzzy Time Series Prediction Model and Application based on Fuzzy Inverse

This paper presents a forecast model of fuzzy time series prediction to study the prediction of enrollment. It takes the percentage of enrollment changes as the domain, constructs the inverse fuzzy number and predicts the enrollment of Qiong Zhou University from 2005 to 2013. Compared with the existing models, the mean square error and prediction error of the ameliorated model are smaller and the precision is higher. The authors get the better method to solve the prediction problem based on the inverse fuzzy prediction model.

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.

Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

Biofuel production in China suffers from many uncertainties due to concerns about the government’s support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel’s characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey–Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey–Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.

Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system

In this paper, a system, namely the adaptive neuro-fuzzy inference system (ANFIS), is investigated and used for fuzzy nonparametric regression function prediction with crisp input and fuzzy output. The fuzzy least squares problem based on Diamond׳s distance is proposed to optimize the consequent parameters in the hybrid algorithm of the adaptive neuro-fuzzy inference system method. Also, an algorithm is proposed to reduce bias and the boundary effect of the estimates of the underlying regression function. Various examples are used to illustrate and test the performance of this approach. The proposed method is compared with the local linear smoothing method for investigating the accuracy of the approach. The results demonstrate that the proposed method not only gives less biased estimates of the center line, the lower and the upper limit lines of underlying fuzzy regression function but also reduces bias and the boundary effect of the estimates of the underlying regression function by using the proposed algorithm significantly.

Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams

The Near Surface Mounted (NSM) technique is a promising strengthening approach, though unpopular due to reliable serviceability prediction options. In this paper, a soft computing technique is employed to predict the deflection and cracking behavior of NSM strengthened RC beams. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to simulate the serviceability behavior of one control and six NSM strengthened RC beams using variable bond lengths (1600, 1800 or 1900mm) with different NSM strengthening materials (steel and CFRP bars). The proposed ANFIS model employed variable load and bond length as inputs while the output parameters were deflection and crack width of the steel and CFRP bars. The ANFIS results were compared with the experimental results using the root-mean-square error (RMSE), coefficient of determination and Pearson coefficient. The results found that the ANFIS approach showed an improvement in predictive accuracy and generalization capability in comparison to the fuzzy approach. The highest level of accuracy with ANFIS was achieved when predicting CFRP bar crack width, where RMSE=8.05E−07. In contrast, the lowest level of accuracy was achieved when predicting CFRP bar deflection, with RMSE=0.045185.

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment.

The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.

Time-variant structural fuzzy reliability analysis under stochastic loads applied several times

A new structural dynamic fuzzy reliability analysis under stochastic loads which are applied several times is proposed in this paper. The fuzzy reliability prediction models based on time responses with and without strength degeneration are established using the stress-strength interference theory. The random loads are applied several times and fuzzy structural strength is analyzed. The efficiency of the proposed method is demonstrated numerically through an example. The results have shown that the proposed method is practicable, feasible and gives a reasonably accurate prediction. The analysis shows that the probabilistic reliability is a special case of fuzzy reliability and fuzzy reliability of structural strength without degeneration is also a special case of fuzzy reliability with structural strength degeneration.

Control of input delayed pneumatic vibration isolation table using adaptive fuzzy sliding mode

PNEUMATIC ISOLATORS ARE PROMISING CANDIDATES FOR INCREASING THE QUALITY OF THE PRECISION INSTRUMENTS. FOR THIS PURPOSE, HOWEVER, HIGHER PERFORMANCE OF SUCH ISOLATORS IS A PREREQUISITE. IN THIS PAPER AN ADAPTIVE FUZZY SLIDING MODE CONTROLLER IS PROPOSED TO IMPROVE THE PERFORMANCE OF A PNEUMATIC ISOLATOR IN THE LOW FREQUENCY RANGE, I.E., WHERE THE PASSIVE TECHNIQUES HAVE OBVIOUS SHORTCOMINGS. THE MAIN IDEA IS TO COMBINE THE ADAPTIVE FUZZY CONTROLLER WITH ADAPTIVE PREDICTOR AS A NEW TIME DELAY CONTROL TECHNIQUE. THE ADAPTIVE FUZZY SLIDING MODE CONTROL AND THE ADAPTIVE FUZZY PREDICTOR HELP TO CIRCUMVENT THE INPUT DELAY AND NONLINEARITIES IN SUCH ISOLATORS. THE MAIN ADVANTAGE OF THE PROPOSED METHOD IS THAT THE CLOSED-LOOP SYS-TEM STABILITY IS GUARANTEED UNDER CERTAIN CONDITIONS. SIMULATION RESULTS ARE COMPARED TO THE RESULTS OF TDC TECHNIQUE TO VERIFY THE EFFECTIVENESS OF THE PROPOSED METHOD.

Packet Forwarding Misbehaviour Isolation using Fuzzy Trust-based Secure Routing in MANET

MANETs are much more susceptible to various attacks because of openness in network topology and being away of a centralized administration in management. As an outcome of that, more malicious nodes are often comes in and goes out without being detected from the network topology. Hence, MANET needs very specialized security methods to isolate the false entrance. As well as there is no single solution that fitting in different types of the network where the nodes can be behave like any apparatuses. The networks works well if the nodes are trusty and act rightly cooperatively. In order to improve the security of the network, this paper gets started the new interesting approach to evaluate the trustworthiness of the nodes. Fuzzy Trust-based Secured Routing (FTSR) approach provides a flexible and feasible approach to choose trusted route to meet the requirement of the security of the data transmission. In this, fuzzy logic rule prediction mechanism is adopted to notice the future behavior of node by updating the node's trust. We have also analyzed the performance metrics such as packet delivery ratio, end-to-end delay and average throughput which can also increased accordingly through newest approach.

The estimation of fuzzy boundary of multioperational machine tool life

The purpose of this paper is to present the basic principles of the construction and operation of monitoring systems of the tool condition, and the machine tooling backup man-agement in the computer-aided manufacturing. The proposed approach is focused on creating conditions for the effective tool state management. For this, the application of the intelligent host control module on the basis of the interactive electronic technical documentation of the mechatronic technological object is proposed. Valuation principles of the tool life fuzzy boundary are fully considered on the basis of the of fuzzy knowledge theory. Three methods for estimating the parameters of the resistance fuzzy boundary are presented. A neural network which operates on the basis of the developed fuzzy prediction rule base of the cutting tool wear depending on the instrument status and its impact on the part cutting accuracy is built. The results obtained are applicable in the sphere of the industrial engineering.

Fuzzy comprehensive prediction of fabric stiffness handle based on quasi-three-point restraint test

A new method of measuring and evaluating fabric stiffness handle based on the comprehensive handle evaluation system for fabrics (CHES-FY) is introduced. The CHES-FY system can evaluate multiple mechanical properties and assess the fabric handle by a pulling-out test. In this article, a fuzzy comprehensive evaluation model is established to grade fabric stiffness handle based on the feature indices extracted from the pulling-out force and displacement curve of fabric measured by CHES-FY system. The entropy method is suggested to determine the weighting-factor vector of feature indices. The fuzzy conversion matrix is obtained on a series of membership functions that are achieved by investigating 47 fabric samples for feature indices. The correlation of fuzzy evaluation, subjective evaluation and the Kawabata primary hand evaluation is estimated statistically. The results show that the fuzzy model with entropy method resulting from the selected feature indices provides a simple and effective stiffness handle grading method and the evaluation method of CHES-FY system appears to closely estimating the fabric tactile response the human fingers feel when touching and pinching wear fabric.

Use of fuzzy set theory for minimizing overbreak in underground blasting operations – A case study of Alborz Tunnel, Iran

In order to increase the safety of working environment and decrease the unwanted costs related to overbreak in tunnel excavation projects, it is necessary to minimize overbreak percentage. Thus, based on regression analysis and fuzzy inference system, this paper tries to develop predictive models to estimate overbreak caused by blasting at the Alborz Tunnel. To develop the models, 202 datasets were utilized, out of which 182 were used for constructing the models. To validate and compare the obtained results, determination coefficient (R2) and root mean square error (RMSE) indexes were chosen. For the fuzzy model, R2 and RMSE are equal to 0.96 and 0.55 respectively, whereas for regression model, they are 0.41 and 1.75 respectively, proving that the fuzzy predictor performs, significantly, better than the statistical method. Using the developed fuzzy model, the percentage of overbreak was minimized in the Alborz Tunnel.

Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

An energy management system (EMS) determines the dispatching of generation units based on an optimizer that requires the forecasting of both renewable resources and loads. The forecasting system discussed in this paper includes a representation of the uncertainties associated with renewable resources and loads. The proposed modeling generates fuzzy prediction interval models that incorporate an uncertainty representation of future predictions. The model is demonstrated using solar and wind generation and local load data from a real microgrid in Huatacondo, Chile, for one-day ahead forecasts to obtain the expected values together with fuzzy prediction intervals to represent future measurement bounds with a certain coverage probability. The proposed prediction interval models would help to enable the development of robust microgrid EMS.

NOVEL HYBRID RULE NETWORK BASED ON TS FUZZY RULES

A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classication and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically com- bine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.

NEO-Fuzzy State-Space Predictive Control

This paper describes the development of a novel state-space model predictive controller. The proposed modelling structure used to capture and predict the nonlinear process dynamics lies on the concept for a neo-fuzzy neuron, deployed in state-space. The introduced approach represents a set of simple fuzzy inferences along the temporal behaviour of each input node, whose dynamics is expressed as a singleton function. The learning algorithm for the proposed modelling structure is realized as a gradient descent procedure. On the basis of the obtained neo-fuzzy state-space model, a fuzzy predictor for the purpose of predictive control is developed. The achieved predictions are used to optimize the future system response by implementing a quadratic programming optimization procedure along the stated controller horizons. The potentials of the proposed approach are studied by simulation experiments to modelling and control of a nonlinear drying plant.

Design of Explicit Fuzzy Prediction Controller for Constrained Nonlinear Systems

This paper presents an explicit fuzzy predictive control method for a class of nonlinear systems with constrained inputs. The main idea is to construct a terminal invariant set and explicit predictive controller with affine input on the basis of T-S fuzzy model. This method need not compute the complex nonconvex nonlinear programming problem of earlier nonlinear predictive control methods and decreases the number of optimization variables and guarantees stability of the closed-loop system. The simulation results on a numerical example show the validity of the method presented in this paper.

The Design of Kiln Liquid Level Control System with Long Time-Delay

In the glass production industry, kiln glass liquid level control system is an important link. And the controlled object of liquid level control system often has the characteristics of nonlinear, time-varying and long time-delay. For this kind of complex control system, the parameters of the PID controller are difficult to be set and it is difficult to obtain the expected control effect. This paper puts forward combining fuzzy controller and Smith predictor, expounds the complementary action between Smith predictor controller and fuzzy controller. Finally according to the characteristics of the glass kiln liquid level fluctuation and the actual situation, the reasonable glass kiln liquid level automatic control scheme is designed. Using Simulink tool in MATLAB to do the simulation for the system and through the analysis of simulation results, this control system can achieve good control effect.