Fuzzy Prediction Research Articles

A fuzzy-based customer response prediction model for a day-ahead dynamic pricing system

Demand side management is an important aspect of the newly established smart grid concept. Consequently, real-time pricing plays a crucial role in controlling the power demand in the grid, as it encourages customers to shift their time-insensitive loads to off-peak periods. However, most solutions do not address the realistic behavior of customers. In this paper, customers’ behavior is modeled based on two main factors; their awareness to the concept of real-time pricing, and their flexibility in shifting their consumption patterns. A Fuzzy logic Customer Response Prediction Model is presented to predict the customers’ demand based on the aforementioned factors and the level of motivation offered by the utility company in terms of a change in price. Unlike most of the research in the field which is concerned with predicting the demand, the core of this model lies in predicting how different customers will behave in a real-time pricing environment based on their levels of flexibility, awareness, and the added motivation, which in turn will shape the overall future demand. Using the presented model, it is expected that customers with higher awareness and flexibility would respond better to dynamic price changes.

Temperature Monitoring and Forecast System in Remote Areas with 4.0G LTE Mobile Technologies

The need to monitor areas of high risk in terms of temperature indexes has included two important elements for its compliance: monitoring and forecast of records in an environment. Performing this procedure manually is inefficient as it provides a flat perspective and can’t predict the state of the environment with rigor. Software systems are contemporary elements in constant refinement, which satisfy emerging needs of a context, so that, in relation to monitoring and forecast an environment, it allows a sophisticated automation of the process, and that tends to lead to a better supervision of the risks in the environment. This article presents a proposal for the supervision of high-risk areas, through temperature registers, manageable through the design of a software system with the implementation of mobile 4.0G LTE technologies, aimed at efficiency and effectiveness in the notification of environmental status. Finally, I conclude with a remote temperature monitoring and forecast system, using mobile technologies, with a fuzzy logic prediction system with a quadratic error not greater than 2.6%, that is, on a fuzzy algebra system whose Numerical calculation does not exceed this error value in comparison with actual values; At the same time that the future works are presented from the approach of the research that is postulated, according to the emergence of new perspectives related to this developing software system.

The Process of Engine Reliability Fuzzy Prediction

Since reliability is one of the most important indicators of engines, reliability research has been increasing used in engines widely. These studies are based on the failure data which are obtained during working time, and the distribution can be summarized by using computer aided technology, hence improving the product reliability. With the help of computer aided technology, the reliability fuzzy prediction method of the engine system, for example, is used in supercharging system to verify the feasibility.

Method of Predicting the Deadline of Spacecraft Orbit “Life” Taking into Account Progressive Contamination in Near-Earth Space by Space Debris

This paper highlights the features of modeling the process of mechanical contamination of near-Earth space (NES), gives a brief description of existing models for predicting the mechanical contamination of NES, and analyzes the influence of uncertainty in the initial data on the accuracy of predictions of the mechanical contamination of NES by modern models. It has been proposed to predict the deadline of the “ecological life” of near-Earth orbit based on a description of increasing the density of mechanical contamination in NES with logistic dependences. The analytic function has been obtained and presented for the first time, allowing one to obtain the predicted value of the deadline of the near-Earth orbit in the form of an equation that includes the value of the maximum permissible contamination density given in the form of a fuzzy value. The solution of this fuzzy equation in the case of a triangular shape of the membership function of the maximum possible contamination density has been presented. The possibility of transforming the fuzzy prediction to a crisp interval form has been shown by finding the interval crisp alpha-level set closest to the considered fuzzy set (FS).

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

RETRACTED: T-S fuzzy prediction on competitive structure model and evaluation system of emerging industry innovation alliance

Abstract In order to improve the effectiveness of research on the evaluation system of emerging industry, a competitive analysis model of industry innovation alliance based on the simultaneous evaluation of fractional T-S fuzzy system is proposed. Firstly, considering the relevant requirements of industry transformation and upgrading, establish a structural evaluation model for the competitiveness of emerging industry innovation alliance; secondly, give the T-S fuzzy prediction and prove the asymptotic stability theorem of prediction system, and on this basis, prove the asymptotic stability of the fractional T-S fuzzy error system, and give the selection method of the gain matrix, to achieve effectiveness of the structural evaluation model for the competitiveness of the industry innovation alliance; finally, the effectiveness of the proposed algorithm is verified by empirical analysis.

Decentralized adaptive force/position control of reconfigurable manipulator based on soft sensors

Two soft sensor control methods are proposed to deal with force/position control of reconfigurable manipulator without using wrist force sensors. First, modeling uncertainties and coupled interconnection terms between the subsystems are approximated by using adaptive radial basis function neural network, and the soft sensor model of the contact force is established by means of adaptive radial basis function neural network to design hybrid force/position controller. Then, a decentralized explicit force controller based on impedance inner control is designed. The reference trajectory of impedance inner controller is provided by explicit force controller based on the fuzzy prediction, and the soft sensor model of the contact force is established by the fuzzy system. The proposed soft sensor models do not request the exact mathematical relationship between the contact force and auxiliary variables and provide a feasible method to replace the wrist force sensors which are expensive and easily influenced by the external factors. Compared with the observer method, the proposed soft sensor methods do not depend on the knowledge about the model of reconfigurable manipulator, so provide better position and force tracking precision.

Energy management system design and testing for smart buildings under uncertain generation (wind/photovoltaic) and demand

This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization. A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

Adaptive estimation of Haar wavelet transform parameters applied to fuzzy prediction of network traffic

Abstract In this paper, we propose an adaptive approach to estimate the energies of the wavelet and scale coefficients of the Haar wavelet transform used in the multifractal modeling of network traffic traces. Simulation results confirm that the estimates obtained for the modeling parameters in the wavelet domain are precise. In addition, we propose an equation to calculate the autocorrelation function of the underlying multifractal model in terms of these wavelet domain parameters. In order to enhance the prediction performance of network traffic traces, the autocorrelation function is used to update orthonormal basis functions in a fuzzy system. To validate the adaptive fuzzy prediction approach, simulations with real network traffic traces are carried out, showing that the proposed algorithm provides lower mean square errors than other algorithms in the literature.

A hybrid fuzzy inference prediction strategy for dynamic multi-objective optimization

Many real-world multi-objective optimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently track the Pareto front in dealing with dynamic environments. In this paper, a hybrid population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is presented to extrapolate ahead the trajectory (position and/or orientation) of the new Pareto optimal solution set from the previous Pareto optimal solution sets and ensure the algorithm to respond quickly and effectively when the environment changes thus tracking the changing Pareto front. In our algorithm, the fuzzy inference model based on the Maximum Entropy Principle is extracted automatically from the previously found Pareto optimal solution sets to predict the Pareto solution sets at the beginning of the next time. Moreover, a new one-step prediction model is proposed to improve the prediction accuracy for environmental changes from motion state to static state and vice versa. Furthermore, a new variant of teaching–learning-based optimization algorithm with decomposition is first proposed as the MOEA optimizer for solving dynamic multi-objective optimization problems (DMOPs). In the proposed MOTLBO/D variant, the multi-objective decomposition mechanism is adopted and neighbor strategy is introduced into teaching–learning-based optimization algorithm (TLBO) to maintain the diversity of population and avoid the algorithm trapping into the local areas. Finally, to verify the performance of the proposed methods, ten benchmark test functions are simulated and evaluated. The statistical results indicate that the proposed FIOPPS strategy is promising for dealing with DMOPs.

An algorithm of Saxena-Easo on fuzzy time series forecasting

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

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.

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.

Ordered weighted averaging operator used to enhance the accuracy of fuzzy predictor based on genetic algorithm

In this paper, we have proposed a novel concept to optimise ordered weighted aggregation (OWA) based fuzzy time series predictor (FTSP) using genetic algorithm (GA). Firstly, accurateness of FTSP is enhanced by applying effective method of aggregation on past observations using OWA weights. These weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing monotonic (RIM) quantifiers. Subsequently, GA is used to optimise membership functions of FTSP by generating its wide range of parameters in the region of time series. Lastly, this model is capable of controlling its performance by varying GA parameters. To assess proposed method, we used dataset of enrolments and outpatient visits, as used by almost all previous research in this domain. Evaluation results indicate coalescing OWA and GA for FTSP significantly reduced mean square error (MSE) and average forecasting error rate (AFER).

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.

Research on Reliability Modeling, Allocation and Prediction of Chemical Production System

Reliability allocation and prediction play an important role in the chemical production system, controlling the significance of each production allocation and predicting system reliability to analyze the designs in each system or integrated system if meeting requirements or not. This paper takes an ethylene plant as an example to study reliability modeling, allocation and prediction of related system. It is aimed to allocate and predict each production system or unit. In terms of system reliability allocation, it finds out the reliability allocation R in integrated system is 0.76460 with improved fuzzy analytic hierarchy process (AHP). Due to the number higher than initial reliability value of 0.73886, it illustrates the integrated reliability allocation meets the design requirements. In terms of reliability prediction, the result is more accurate when using Bayes fuzzy reliability prediction to calculate system reliability level and it can reflect this kind of indeterminate small sample data better.

An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic‐flow modelling, local segment traffic congestion prediction, and origin‐destination traffic congestion service for drivers. Macroscopic model‐based traffic‐flow factors were formalized on the basis of the analysis results. Fuzzy rules‐based local segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine‐Hugoniot condition and an origin‐destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.

Security aware dual authentication-based routing scheme using fuzzy logic with secure data dissemination for mobile ad-hoc networks

The security of mobile ad-hoc networks (MANETs) has been getting a great deal of consideration in the field of wireless networking because MANETs are vulnerable to malicious attacks. Secure transmission without node failure in MANETs is a testing issue because of the nonappearance of centralised administration, and openness in the network topology. In order to upgrade the security of network and shield the nodes from vulnerabilities, this paper proposes a highly efficient dual authentication routing scheme which utilises fuzzy logic prediction rules to select the most reliable path and the proposed scheme coordinates with cipher text policy-attribute-based encryption (CP-ABE) for secure data dispersal among the source to destination with game theory-based jammer attack detection model. The performance of the proposed scheme is assessed with respect to different evaluation metrics in different node density levels. The results obtained clearly describes the proposed scheme provides a flexible and feasible decision to select the route that meets the security requirement of data packets transmission in MANETs environment.

Semantic Predictive Model of Student Dynamic Profile Using Fuzzy Concept

Student profile describes the best way a student prefers to learn. It includes information on student’s characteristics such as background knowledge, learning preference, styles, interest, goals etc. The major challenge that the students face in learning system is that they are unable to retrieve relevant information based on their requirements. One of the methods used to obtain the requirement of the students is to construct an efficient student profile which would reflect the true student needs. The proposed work is to develop an intelligent ontology-based dynamic student profile that provides semantic retrieval using fuzzy concepts. The approach starts with the collection of both static and dynamic data of students. The dynamic dataof students particularly student interest and learning style are obtained by weblog analysis using algorithms such as semantic based representation using WordNet and decision tree classifier algorithm based on Felder-Silverman learning style model (FSLSM). The retrieved data is then used to construct student profile using ontology in which automatic student profile updating is obtained using ontology -based semantic similarity algorithm using WordNet. Finally, semantic retrieval of student information from ontology is achieved by integrating fuzzy concepts using fuzzy linguistic variable and ‘fuzzy IF THEN’ rules. Fuzzy linguistic variable is used to make precise representation on the existing ontology concepts which facilitate more specific classification and semantic retrieval of information. The predictive model of student profile is designed with the implementation of ‘fuzzy IF THEN’ rules using forward chaining reasoning process in the existing ontology model. The inference engine predicts the preference of a new student based on the reasoning process done for specific conditions particularly on student interest and learning style. The experiments were performed using NetBeans IDE, OWL API and Protégé 4.2 beta editor. The experiment result shows the successful completion of student profile generation, updating, fuzzy semantic retrieval and prediction through utilization of fuzzy concepts in student profile ontology.