Fuzzy Inference Procedure Research Articles

Fuzzy linguistic forecasting of the results of the implementation of regional socio-economic projects

The article is devoted to the problem of improving the intellectual digital support for managing the implementation of regional socio-economic projects, which are an effective mechanism for the development of regions. Given the high level of uncertainty in the description of the subject area, it is proposed to use expert technologies to assess the degree of achievement of the intermediate and target indicators of the project. L. Zadeh's linguistic modeling apparatus was used as the basis for intellectual tools for representing and processing expert judgments. A linguistic indicative-temporal-territorial model of the project has been built in the form of a set of linguistic variables and a system of fuzzy production rules linking these variables. At the same time, temporal and territorial linguistic variables reflect the time and place of measurement of project indicators, the incomplete correspondence of which determines the degree of reliability of the measured values of indicators. The use of the constructed system of indicative-temporal-territorial fuzzy production rules makes it possible to link the intermediate values of the indicators with the target values of the project indicators and thereby predict the possible outcomes of its implementation. For this, a fuzzy logic inference procedure based on the use of the Mamdani algorithm has been developed. Preliminary results of using a research prototype of an intelligent expert system for analyzing the intermediate results of the project stages and predicting its results may indicate the effectiveness of the proposed approach.

Using fuzzy inference to plan capital repairs and reconstruction of public buildings

Introduction. The article analyzes the determinants that condition the incorporation of public buildings into capital repair and reconstruction plans (CRR plan). These include the technical condition of a facility, specified in an expert opinion and identified on the basis of the survey results, the duration of effective operation, determined in accordance with guideline values, the work performance time, the availability of the engineering and transport infrastructure, and other factors.
 Materials and methods. The co-authors formulated fuzzy inference rules and compiled a table of expert opinions issued on their basis. To implement the fuzzy inference procedure, the co-authors applied the Mamdani algorithm, the min-conjunction for aggregating indicators, the max-disjunction for accumulating conclusions, and the centre of gravity method to ensure defuzzification. The Editor of Fuzzy Output Systems of the MatLab package is used to implement the proposed fuzzy inference pattern. The Saati hierarchy method is used to design membership functions (FP). The analysis of literature sources did not identify any works in which fuzzy logic methods or fuzzy inference rules were used to plan the CRR of public buildings.
 Results. Membership functions for all factors and the final indicator on the selected scale, corresponding to the Harrington desirability function in the range of values [0; 100], are designed. The results of the implementation of the proposed fuzzy inference system in the MatLab environment are presented in the form of graphs and numerical values of all input linguistic variables.
 Conclusions. Fuzzy inference allows to obtain the numerical value of the integral repair potential that underlies an informed decision about the incorporation of a facility into the CRR plan. The strength of the approach is the modifiability and expandability of the rule base in practical work. The proposed planning tool allows to consider a combination of principal factors, cut costs, reduce the time frame and improve the public building repair quality.

MODELS AND FUZZY MICROCLIMATE CONTROL SYSTEM IN THE STORAGE OF ARCHIV GE OF ARCHIVAL DOCUMEN AL DOCUMENTS

In this article, the main object of research is the creation of appropriate microclimatic conditions to ensure reliable and high-quality storage of archival documents, as well as automatic control of the optimal values of the main parameters of the external and internal environment that directly affect the quality of storage. To control the microclimate, three categories of models for automatic control of these parameters are considered separately in the archives: the “white box”, “black box” and “gray box " models. The results of the analysis of the advantages and disadvantages of the considered models are presented. The generalized structure of the microclimate management system is also given, as well as a list of controlled and changeable parameters of the microclimate management system of archives. It is proposed to use the fuzzy logic apparatus to create microclimate control systems in archival repositories, which allows synthesizing stable algorithms for its functioning in conditions of uncertainty. The specific steps that need to be performed when designing and using fuzzy inference systems and which are implemented based on the rules of fuzzy logic are listed. When designing and using fuzzy inference systems, it is necessary to observe certain stages that are implemented based on the rules of fuzzy logic. A generalized algorithm for forming a rule base with a technique for implementing the fuzzy inference procedure is presented. The tasks that need to be solved when designing a fuzzy control system are indicated. A system of automatic temperature control in archival repositories with a fuzzy logic controller is presented.

Optimum planning of experimental research at the biogas plant

Biogas energy, being one of the important components of ecological agricultural production, on the one hand, allows you to conduct successful disposal of crop and livestock waste, receiving organic fertilizers, and on the other hand makes it possible to provide heating and electricity to greenhouses, farms and other industrial buildings and buildings. At the same time, bioenergy technologies require improvement and development, taking into account the specifics of the agro-industrial complex in which they are used. The problems of organizing scientific research at a biogas station are considered. Based on the analysis of the functioning of the biogas plant, a mathematical formulation of the problem of constructing a schedule for conducting experiments taking into account the costs of equipment retooling is formulated. To determine the coefficients of the objective function of the constructed optimization problem, it is proposed to use expert technologies and a fuzzy inference procedure based on a fuzzy production model of knowledge about the subject area. To construct a solution to the optimization problem, along with the classical methods of discrete linear optimization, it is proposed to use evolutionary genetic algorithms that are effective in solving problems of large dimension.

Fuzzy multivariate forecasting of the implementation of regional development programs for the agro-industrial complex

The article is devoted to the problems of improving digital intellectual tools for managing the implementation of socio-economic and technological programs aimed at developing the agro-industrial cluster of the regional economy. The aim of the work is to develop a procedure for forecasting the implementation of programs based on the data of the previous stages and knowledge, reflecting the specifics of agricultural production. To describe the indicators of the current and projected state of the regional agro-industrial complex, it is proposed to use the apparatus of the theory of linguistic variables, which makes it possible to use expert technologies for filling the knowledge base and allows us to take into account the high level of uncertainty characteristic of the agricultural market. The links between current and projected performance are represented by fuzzy production rules. The fuzzy inference procedure used in forecasting (based on the Mamdani algorithm) is built in the form of an interpreted fuzzy multilayer neural network. The preliminary results of using the developed procedure as part of a research prototype of an information-analytical system may indicate its effectiveness. The practical significance of the developed toolkit is due to the possibility of its use as a means of intellectual support for making scientifically grounded management decisions on the implementation (taking into account possible adjustments) of development programs for the regional agro-industrial complex.

Technologies for Making Reliable Decisions on a Variety of Effective Factors using Fuzzy Logic

Problem of choosing methods has been presented for increasing the reliability of solutions while reducing the time resource for researching the state space of a particular object. Effective factors that can significantly affect the attainability of decision-making goals in a fuzzy process development environment are identified. Presented the selected factors in a classic form in natural language, which significantly increases the confidence in the decisions made by determining the maximum, minimum values of membership functions and the set of criteria by using manifest conflict situations when deciding. Information technologies and approaches for identifying the completeness of decision-making goals on a variety of effective factors are proposed. The effectiveness of the proposed solutions using the fuzzy inference procedure and the Zade-Mamdani’s approach is estimated. Software testing carried of the described methods for evaluating and deciding on a variety of factors out using object-oriented programming. Experimental testing of realized ideas has confirmed an increase in the reliability of making aim management decisions in various applied subject areas.

Application of fuzzy modeling in the evaluation of crisis state of enterprises

The features of the proposed fuzzy model for assessing the crisis state of enterprises have been disclosed. The MATLAB software environment has been selected as the environment for building a fuzzy output system. In the model of a fuzzy assessment of the crisis state of enterprises, the following input linguistic variables have been highlighted: the relative level of financial status, the probability of bankruptcy, the level of information security, the level of innovation potential. The terms of the input variables and the result variable have been described. The rule base for fuzzy inference system has been formulated. The results of modeling the assessment of the crisis state of enterprises have been represented by a fuzzy inference procedure.

Application of Wavelet Neuro-Fuzzy System (WNFS) method for stock forecasting

Forecasting is a very important element in decision making, because the effectiveness of a decision, generally depends on several factors that we cannot see when the decision was taken. In this study, the Wavelet Neuro-Fuzzy System (WNFS) model that combines wavelet transformation and neuro-fuzzy techniques is applied to forecast daily closing stock price data of BMRI.JK. The observed daily stock price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for daily forecasting stock price for three days in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno model. The result shows that the presence of wavelet input in Neuro-Fuzzy System, can provide optimal prediction in daily stock price data, with small error value of predicted result. This would be helped investors or economists to produce meaningful information in either buy or sell a stock.

Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging

Fuzzy logic controllers are readily customizable in natural language terms and can effectively deal with nonlinearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decision-making system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVIEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant-charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.

Прогнозування екологічного стану міської транспортної системи з використанням нейронних мереж

Мета: Метою цієї роботи є розробка ефективної моделі оцінювання екологічного стану міської транспортної системи з використанням загальних принципів нейронних мереж. Методи дослідження: Запропонована модель основана на роботі двох нейронних мереж з урахуванням впливу щільності транспортного потоку та рівня транзитного потенціалу на урбанізовані райони. Результати: На основі синтезу загальних принципів теорії нечітких множин та нейронних мереж розроблено модель оцінки рівня екологічного стану міської транспортної системи. Дано графічне представлення моделі. Високий ступінь достовірності прогнозу забезпечується навіть при низьких швидкостях навчання і високій динаміці змін статистичних даних в умовах транзиту транспортних потоків міста. Висновки: Використання нечітких нейронних мереж забезпечує можливість встановлювати повну відповідність між математичним представленням процедури нечіткого висновку і структурою міської транспортної системи. Запропонована модель дозволяє сформулювати чіткі екологічні орієнтири при прийнятті рішень в області транзитних перевезень з урахуванням інтересів підприємств, транспорту і населення міста, з подальшим перерозподілом транспортних потоків в тимчасовому і географічному просторі промислових районів міста.

PENERAPAN METODE WAVELET NEURO-FUZZY SYSTEM (WNFS) DALAM MEMPREDIKSI HARGA BERAS DUNIA (Studi Kasus: Harga Beras Thailand sebagai Harga Acuan Dunia)

Rice trade is one of the food resistance components in terms of its availability. The comprehensive integration between international commodity rice prices and domestic prices encourage the prediction of world rice prices, using the Thai rice price as the world's reference price. In this study, the wavelet neuro-fuzzy system which combines the wavelet transform and the neuro-fuzzy technique has been applied to monthly predict the world rice price. The observed monthly rice price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), and then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for monthly predicting world rice prices for six months in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno zero-order model. Obtained results showed that the WNFS method can be used to predict the world rice price, with the error value resulted from learning process of MSE 20,69097 and MAPE 0,65584%. While the error measurement results for the six months in advance prediction shows the acquisition of MSE 3610,14847 and MAPE 13,62334%. Keywords : Prediction of Monthly World Rice Price, Maximal Overlap Discrete Wavelet Transform, Neuro-fuzzy System.

Методи організації паралельних обчислень для багаторівневих нечітких систем Такагі – Сугено

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

Dynamic Quality Index for agricultural soils based on fuzzy logic

The increasing number of successful applications of fuzzy logic and fuzzy sets theory to dealing with the uncertainty, imprecision and subjectivity inherent to environmental quality assessments, and the recent development of new procedures based on fuzzy logic for the design of environmental quality indexes open new ways to carry out more rigorous and realistic estimations of soil quality. With these considerations in mind, the aim of this work is to design an index based on fuzzy logic, which is especially addressed to assess the dynamic quality of agricultural soils – Soil Dynamic Quality Index (S-DQI). This index is described by a group of three indexes (S-DQIPHYS, S-DQICHEM, S-DQIBIOL), each one designed to evaluate the dynamic quality of agricultural soils with regard to their physical, chemical and biological characteristics, respectively. Each index is determined from the joint opinion of a panel of experts, which decides: (i) the attributes or properties of soil which determine its dynamic quality for farming; (ii) the most suitable indicator for quantifying each of them; (iii) the influence of the values taken by these indicators on the quality of agricultural soils, which is expressed by means of membership functions, and (iv) the relative importance of the attributes in the respective index, which is expressed by means of normalized priority vectors. The value of each of these indexes is finally obtained as a result of a fuzzy inference procedure, which is a crisp value ranging from 0 to 1. This procedure allows us to express the values taken by the indicators in a particular agroecosystem by means of both crisp values and fuzzy numbers, the latter being frequently a more rigorous and realistic way of representing the estimations of the soil properties in any emplacement. Verification tests show the satisfactory response capability of the index to changes in the soil properties. The use of the designed S-DQI for routine monitoring of the quality of farming soil allows the estimation of the changes induced in the soil due to use, which is helpful to assess systematically the sustainability of the agricultural practices.

Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis

The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage–current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations.

Integrating Decision Analysis with Fuzzy Programming: Application in Urban Water Distribution System Operation

AbstractWater-distribution operation is an essential part of an urban water-supply system to deliver high-quality water to consumers. Management of such an operation may involve deliberations of operation cost, system capacity, and environmental restriction, and is convoluted with many forms of uncertainties. In this paper, an integrated fuzzy programming and decision analysis (IFPDA) approach was proposed for a multilayer urban water-distribution system management under uncertainty. The system consisted of two water sources, four treatment plants, seven reservoirs, and seven consuming zones. Uncertain information associated with water demands, water-treatment capacities, water-transfer cost, and leakage rate was described by a trapezoidal-shaped fuzzy set and embedded into a fuzzy programming framework. The balance between the satisfaction degree of achieving the system objective and feasibility level of meeting the related constraints was analyzed by fuzzy inference procedures. The results indicate that...

Development of environmental quality indexes based on fuzzy logic. A case study

Fuzzy logic is a suitable mathematical tool to treat information of very different nature, which is partially affected by uncertainty and subjectivity. Its successful application in the environmental field in recent years shows its great potential for the design of environmental quality indexes.This work deals with a rigorous and versatile methodology based on fuzzy logic for the design and the subsequent assessment of environmental quality indexes (EQI). It enables the design of specific indexes to evaluate the quality from different perspectives in any environmental compartment.The information required to design the EQI should be supplied by a panel of experts in all the different aspects related to the quality under assessment. An environmentalist team should coordinate the panel and the acquisition, management and treatment of the information supplied at each step of the procedure. Conventional convergence methods should be used to obtain a joint assessment at each step in order to rigorously integrate the panel's contributions.For the design of the EQI, firstly, a set of attributes A={ai} is defined, which should include all the significant properties influencing the quality under consideration. Next, a conventionally accepted indicator Ii is selected to quantify each attribute ai, together with the range of values [minIi, maxIi]. Next, a normalized indicator Si is calculated for each Ii and a linguistic variable LSi is assigned to each Si. Then, a fuzzy set Bi¯ is defined to each LSi, which describes in fuzzy terms the “Beneficial” contribution of ai to the environmental quality assessed by the EQI. Next, a pair-wise comparison matrix X¯=[x¯ij] of the relative importances of the attributes ai is calculated and subsequently, a standardized priority vector W′={wi′} is determined from matrix X¯, so that each component wi′ provides the relative significance of the attribute ai in the EQI – represented as a fraction of unity. The final value of the index is obtained as result of a fuzzy inference procedure based on the zero-order Method of Takagi and Sugeno. The value of the EQI is a non-dimensional magnitude ranging from 0 to 1, the quality being greater the closer the index is to 1. This methodology is applied to a simplified case study in order to illustrate its practical application, which is the design and assessment of a physical–chemical soil quality index at a particular site.

Application for deinterlacing method using edge direction classification and fuzzy inference system

In this paper, we propose an advanced deinterlacing method which uses filters to estimate the edge direction using luminance information. Subsequently, we are able to obtain the luminance values at for missing pixels. The fuzzy logic concept for image processing is discussed with regard to fuzzy membership function representation and fuzzy inference procedures. The fuzzy if-then rules are employed to conduct the determining edge direction. The use of a different membership function for different direction enables the filter to independently characterize separate influences on pixel variation. Simulation results demonstrate that the proposed method has an enhanced performance, both visually and in terms of the peak signal-to-noise ratio, compared with those of conventional deinterlacing methods.

Human-guided fuzzy decision for image similarity analysis and classification based on information compression

This paper introduces an original unsupervised learning algorithm for information compression that is further used in the proposed fuzzy inference procedure for discovering similarities between different images for the purpose of their classification. Two features extracted from each compressed information model are used in the paper to represent the location of the compressed model in the three-dimensional red-green-blue (RGB) space and its size (volume). A method for tuning the fuzzy inference procedure is proposed in the paper that uses a predefined human preference in the form of a given list of similar images with their approximate similarity levels. Thus the whole computation scheme is a kind of human-guided similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are among the important problems, discussed in the paper. The final goal is to achieve a plausible “human-like'' decision for similarity, when processing large number of images and other pictorial information. The whole proposed computation scheme for similarity analysis and classification is illustrated on a test example of flower images followed by detailed discussions. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression

The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically calculating the degree of similarity between a pair of images. The fuzzy inference procedure can be tuned based on a predefined human preference (list of similar images), thus performing human-assisted similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are questions open to the user to answer. The proposed computation scheme for similarity analysis is illustrated on a test example of 16 flower images and results are discussed.

Fuzzy inference to assess manufacturing process capability with imprecise data

Process capability indices provide numerical measures on whether a process conforms to the defined manufacturing capability prerequisite. These have been successfully applied by companies to compete with and to lead high-profit markets by evaluating the quality and productivity performance. The loss-based process capability index C pm , sometimes called the Taguchi index, was proposed to measure process capability, wherein the output process measurements are precise. In the present study, we develop a realistic approach that allows the consideration of imprecise output data resulting from the measurements of the products quality. A general method combining the vector of fuzzy numbers to produce the membership function of fuzzy estimator of Taguchi index is introduced for further testing process capability. With the sampling distribution for the precise estimation of C pm , two useful fuzzy inference criteria, the critical value and the fuzzy P- value, are proposed to assess the manufacturing process capability based on C pm . The presented methodology takes into the consideration of a certain degree of imprecision on the sample data and leads to the three-decision rule with the four quadrants decision-making plot. The fuzzy inference procedure presented to assess process capability is a natural generalization of the traditional test, when the data are precise the proposed test is reduced to a classical test with a binary decision.