Search results for: fuzzy control

Authors: N. Borjalilu, P. Rabiei, A. Enjoo

Abstract:

Flight Data Monitoring (FDM) program assists an operator in aviation industries to identify, quantify, assess and address operational safety risks, in order to improve safety of flight operations. FDM is a powerful tool for an aircraft operator integrated into the operator’s Safety Management System (SMS), allowing to detect, confirm, and assess safety issues and to check the effectiveness of corrective actions, associated with human errors. This article proposes a model for safety risk assessment level of flight data in a different aspect of event focus based on fuzzy set values. It permits to evaluate the operational safety level from the point of view of flight activities. The main advantages of this method are proposed qualitative safety analysis of flight data. This research applies the opinions of the aviation experts through a number of questionnaires Related to flight data in four categories of occurrence that can take place during an accident or an incident such as: Runway Excursions (RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision (MAC), Loss of Control in Flight (LOC-I). By weighting each one (by F-TOPSIS) and applying it to the number of risks of the event, the safety risk of each related events can be obtained.

Keywords: F-TOPSIS, fuzzy set, FDM, flight safety.

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Authors: D. Uma Devi, P. Seetha Ramaiah

Abstract:

Voting algorithms are extensively used to make decisions in fault tolerant systems where each redundant module gives inconsistent outputs. Popular voting algorithms include majority voting, weighted voting, and inexact majority voters. Each of these techniques suffers from scenarios where agreements do not exist for the given voter inputs. This has been successfully overcome in literature using fuzzy theory. Our previous work concentrated on a neuro-fuzzy algorithm where training using the neuro system substantially improved the prediction result of the voting system. Weight training of Neural Network is sub-optimal. This study proposes to optimize the weights of the Neural Network using Artificial Bee Colony algorithm. Experimental results show the proposed system improves the decision making of the voting algorithms.

Keywords: Voting algorithms, Fault tolerance, Fault masking, Neuro-Fuzzy System (NFS), Artificial Bee Colony (ABC)

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Authors: Y. Harold Robinson, M. Rajaram, E. Golden Julie, S. Balaji

Abstract:

Mobile ad hoc network (MANET) is a self-configuring network of mobile node connected without wires. A Fuzzy Logic Based Collaborative watchdog approach is used to reduce the detection time of misbehaved nodes and increase the overall truthfulness. This methodology will increase the secure efficient routing by detecting the Black Holes attacks. The simulation results proved that this method improved the energy, reduced the delay and also improved the overall performance of the detecting black hole attacks in MANET.

Keywords: MANET, collaborative watchdog, fuzzy logic, AODV.

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Authors: N. Subramanian, C. Murugesan

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The double difference sequence space I2 (M, of fuzzy numbers for both 1 < p < oo and 0 < p < 1, is introduced. Some general properties of this sequence space are studied. Some inclusion relations involving this sequence space are obtained.

Keywords: Orlicz function, solid space, metric space, completeness

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Authors: Anouar Ben Khalifa, Sami Gazzah, Najoua Essoukri BenAmara

Abstract:

The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper, we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprint and palmprint. The results achieved attest the robustness of the proposed approach.

Keywords: Multimodal biometrics, data fusion, Choquet integral, fuzzy measures, genetic algorithm.

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Authors: Besa Muslimi, Miriam A. M. Capretz, Jagath Samarabandu

Abstract:

Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.

Keywords: fuzzy rule extraction, fuzzy systems, knowledgeacquisition, pattern recognition, artificial neural networks.

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Authors: J. Mehena, M. C. Adhikary

Abstract:

Edge detection is one of the most important tasks in image processing. Medical image edge detection plays an important role in segmentation and object recognition of the human organs. It refers to the process of identifying and locating sharp discontinuities in medical images. In this paper, a neuro-fuzzy based approach is introduced to detect the edges for noisy medical images. This approach uses desired number of neuro-fuzzy subdetectors with a postprocessor for detecting the edges of medical images. The internal parameters of the approach are optimized by training pattern using artificial images. The performance of the approach is evaluated on different medical images and compared with popular edge detection algorithm. From the experimental results, it is clear that this approach has better performance than those of other competing edge detection algorithms for noisy medical images.

Keywords: Edge detection, neuro-fuzzy, image segmentation, artificial image, object recognition.

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Authors: Abdallah A. Alshennawy, Ayman A. Aly

Abstract:

The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.

Keywords: Fuzzy logic, Edge detection, Image processing, computer vision, Mechanical parts, Measurement.

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Authors: Phruksaphanrat B.

Abstract:

This research proposes a preemptive fuzzy goal programming model for multi-objective multi-mode resource constrained project scheduling problem. The objectives of the problem are minimization of the total time and the total cost of the project. Objective in a multi-mode resource-constrained project scheduling problem is often a minimization of makespan. However, both time and cost should be considered at the same time with different level of important priorities. Moreover, all elements of cost functions in a project are not included in the conventional cost objective function. Incomplete total project cost causes an error in finding the project scheduling time. In this research, preemptive fuzzy goal programming is presented to solve the multi-objective multi-mode resource constrained project scheduling problem. It can find the compromise solution of the problem. Moreover, it is also flexible in adjusting to find a variety of alternative solutions. 

Keywords: Multi-mode resource constrained project scheduling problem, Fuzzy set, Goal programming, Preemptive fuzzy goal programming.

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Authors: M. K. Pradhan, C. K. Biswas,

Abstract:

In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively

Keywords: Electrical discharge machining, material removal rate, neuro-fuzzy model, regression model, mountain clustering.

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Authors: Yao-Tien Wang, Hsiang-Fu Yu, Dung Chen Chiou

Abstract:

In wireless and mobile communications, this progress provides opportunities for introducing new standards and improving existing services. Supporting multimedia traffic with wireless networks quality of service (QoS). In this paper, a grey-fuzzy controller for radio resource management (GF-RRM) is presented to maximize the number of the served calls and QoS provision in wireless networks. In a wireless network, the call arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. In this paper, we develop a method to predict the cell load and to solve the RRM problem based on the GF-RRM, and support the present facility has been built on the application-level of the wireless networks. The GF-RRM exhibits the better adaptability, fault-tolerant capability and performance than other algorithms. Through simulations, we evaluate the blocking rate, update overhead, and channel acquisition delay time of the proposed method. The results demonstrate our algorithm has the lower blocking rate, less updated overhead, and shorter channel acquisition delay.

Keywords: radio resource management, grey prediction, fuzzylogic control, wireless networks, quality of service.

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Authors: Li Shoutao, Gordon Lee

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Evolutionary robotics is concerned with the design of intelligent systems with life-like properties by means of simulated evolution. Approaches in evolutionary robotics can be categorized according to the control structures that represent the behavior and the parameters of the controller that undergo adaptation. The basic idea is to automatically synthesize behaviors that enable the robot to perform useful tasks in complex environments. The evolutionary algorithm searches through the space of parameterized controllers that map sensory perceptions to control actions, thus realizing a specific robotic behavior. Further, the evolutionary algorithm maintains and improves a population of candidate behaviors by means of selection, recombination and mutation. A fitness function evaluates the performance of the resulting behavior according to the robot-s task or mission. In this paper, the focus is in the use of genetic algorithms to solve a multi-objective optimization problem representing robot behaviors; in particular, the A-Compander Law is employed in selecting the weight of each objective during the optimization process. Results using an adaptive fitness function show that this approach can efficiently react to complex tasks under variable environments.

Keywords: adaptive fuzzy neural inference, evolutionary tuning

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Authors: Parvinder S. Sandhu, Hardeep Singh

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The requirement to improve software productivity has promoted the research on software metric technology. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. CK metric suit is most widely used metrics for the objectoriented (OO) software; we critically analyzed the CK metrics, tried to remove the inconsistencies and devised the framework of metrics to obtain the structural analysis of OO-based software components. Neural network can learn new relationships with new input data and can be used to refine fuzzy rules to create fuzzy adaptive system. Hence, Neuro-fuzzy inference engine can be used to evaluate the reusability of OO-based component using its structural attributes as inputs. In this paper, an algorithm has been proposed in which the inputs can be given to Neuro-fuzzy system in form of tuned WMC, DIT, NOC, CBO , LCOM values of the OO software component and output can be obtained in terms of reusability. The developed reusability model has produced high precision results as expected by the human experts.

Keywords: CK-Metric, ID3, Neuro-fuzzy, Reusability.

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Authors: S. Balaji, E. Golden Julie, M. Rajaram, Y. Harold Robinson

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Network lifetime improvement and uncertainty in multiple systems are the issues of wireless sensor network routing. This paper presents fuzzy based particle swarm optimization routing technique to improve the network scalability. Significantly, in the cluster formation procedure, fuzzy based system is used to solve the uncertainty and network balancing. Cluster heads play an important role to reduce the energy consumption using particle swarm optimization algorithm, the cluster head sends its information along data packets to the heads with link. The simulation results show that the presented routing protocol can perform load balancing effectively and reduce the energy consumption of cluster heads.

Keywords: Wireless sensor networks, fuzzy logic, PSO, LEACH.

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Authors: P. Moeinzadeh, A. Hajfathaliha

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Many firms implemented various initiatives such as outsourced manufacturing which could make a supply chain (SC) more vulnerable to various types of disruptions. So managing risk has become a critical component of SC management. Different types of SC vulnerability management methodologies have been proposed for managing SC risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce SC risk management by proposing an integrated approach. SC risks are identified and a risk index classification structure is created. Then we develop a SC risk assessment approach based on the analytic network process (ANP) and the VIKOR methods under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. By using FANP, risks weights are calculated and then inserted to the FVIKOR to rank the SC members and find the most risky partner.

Keywords: Analytic network process (ANP), Fuzzy sets, Supply chain risk management (SCRM), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR)

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Authors: H. Shayeghi, B. Mohamadi

Abstract:

This paper presents a possibilistic (fuzzy) model in optimal siting and sizing of Distributed Generation (DG) for loss reduction and improve voltage profile in power distribution system. Multi-objective problem is developed in two phases. In the first one, the set of non-dominated planning solutions is obtained (with respect to the objective functions of fuzzy economic cost, and exposure) using genetic algorithm. In the second phase, one solution of the set of non-dominated solutions is selected as optimal solution, using a suitable max-min approach. This method can be determined operation-mode (PV or PQ) of DG. Because of considering load uncertainty in this paper, it can be obtained realistic results. The whole process of this method has been implemented in the MATLAB7 environment with technical and economic consideration for loss reduction and voltage profile improvement. Through numerical example the validity of the proposed method is verified.

Keywords: Fuzzy Power Flow, DG siting and sizing, LoadUncertainty, Multi-objective Possibilistic Model.

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Authors: Arkady Bolotin

Abstract:

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.

Keywords: Categorization, Uncertain medical categories, Binomial regression model, Fuzzy dependent variable, Robustness.

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Authors: Fuyang Peng, Bo Deng, Chao Qi, Mou Zhan

Abstract:

This paper presents the design and implementation of the WebGD, a CORBA-based document classification and retrieval system on Internet. The WebGD makes use of such techniques as Web, CORBA, Java, NLP, fuzzy technique, knowledge-based processing and database technology. Unified classification and retrieval model, classifying and retrieving with one reasoning engine and flexible working mode configuration are some of its main features. The architecture of WebGD, the unified classification and retrieval model, the components of the WebGD server and the fuzzy inference engine are discussed in this paper in detail.

Keywords: Text Mining, document classification, knowledgeprocessing, fuzzy logic, Web, CORBA

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Authors: Aboagela Dogman, Reza Saatchi, Samir Al-Khayatt

Abstract:

In this study, a network quality of service (QoS) evaluation system was proposed. The system used a combination of fuzzy C-means (FCM) and regression model to analyse and assess the QoS in a simulated network. Network QoS parameters of multimedia applications were intelligently analysed by FCM clustering algorithm. The QoS parameters for each FCM cluster centre were then inputted to a regression model in order to quantify the overall QoS. The proposed QoS evaluation system provided valuable information about the network-s QoS patterns and based on this information, the overall network-s QoS was effectively quantified.

Keywords: Fuzzy C-means; regression model, network quality of service

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Authors: G. Murugeswari, A. Suruliandi

Abstract:

This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Psoriasis Area Severity Index value. For classification and segmentation, the Nearest Neighbor algorithm is applied. We have obtained promising results for identifying affected lesions by using the roughness index and severity level estimation.

Keywords: Fuzzy texture feature, psoriasis, roughness feature, skin disease.

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Authors: Sherin M. Youssef, Ahmed Abouelfarag, Noha M. Ghatwary

Abstract:

An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of digital images. The model depends on the human visual characteristics of image sub-regions in the frequency multi-resolution wavelet domain. In the proposed model, a multi-variable fuzzy based architecture has been designed to produce a perceptual membership degree for both candidate embedding sub-regions and strength watermark embedding factor. Different sizes of benchmark images with different sizes of watermarks have been applied on the model. Several experimental attacks have been applied such as JPEG compression, noises and rotation, to ensure the robustness of the scheme. In addition, the model has been compared with different watermarking schemes. The proposed model showed its robustness to attacks and at the same time achieved a high level of imperceptibility.

Keywords: Watermarking, The human visual system (HVS), Fuzzy Inference System (FIS), Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT).

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Authors: Rahmad Wisnu Wardana, Eakachai Warinsiriruk, Sutep Joy-A-Ka

Abstract:

Selecting the most suitable welding process usually depends on experiences or common application in similar companies. However, this approach generally ignores many criteria that can be affecting the suitable welding process selection. Therefore, knowledge automation through knowledge-based systems will significantly improve the decision-making process. The aims of this research propose integrated data envelopment analysis (DEA) and fuzzy credibility constrained programming approach for identifying the best welding process for stainless steel storage tank in the food and beverage industry. The proposed approach uses fuzzy concept and credibility measure to deal with uncertain data from experts' judgment. Furthermore, 12 parameters are used to determine the most appropriate welding processes among six competitive welding processes.

Keywords: Welding process selection, data envelopment analysis, fuzzy credibility constrained programming, storage tank.

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Authors: Debabrata Das, Pankaj Dutta

Abstract:

Along with forward supply chain organization needs to consider the impact of reverse logistics due to its economic advantage, social awareness and strict legislations. In this paper, we develop a system dynamics framework for a closed-loop supply chain with fuzzy demand and fuzzy collection rate by incorporating product exchange policy in forward channel and various recovery options in reverse channel. The uncertainty issues associated with acquisition and collection of used product have been quantified using possibility measures. In the simulation study, we analyze order variation at both retailer and distributor level and compare bullwhip effects of different logistics participants over time between the traditional forward supply chain and the closed-loop supply chain. Our results suggest that the integration of reverse logistics can reduce order variation and bullwhip effect of a closed-loop system. Finally, sensitivity analysis is performed to examine the impact of various parameters on recovery process and bullwhip effect.

Keywords: Bullwhip Effect, Fuzzy Possibility Measures, Reverse Supply Chain, System Dynamics.

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Authors: Remica Aggarwal, Sanjeet Singh

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The decision to recruit manpower in an organization requires clear identification of the criteria (attributes) that distinguish successful from unsuccessful performance. The choice of appropriate attributes or criteria in different levels of hierarchy in an organization is a multi-criteria decision problem and therefore multi-criteria decision making (MCDM) techniques can be used for prioritization of such attributes. Analytic Hierarchy Process (AHP) is one such technique that is widely used for deciding among the complex criteria structure in different levels. In real applications, conventional AHP still cannot reflect the human thinking style as precise data concerning human attributes are quite hard to be extracted. Fuzzy logic offers a systematic base in dealing with situations, which are ambiguous or not well defined. This study aims at defining a methodology to improve the quality of prioritization of an employee-s performance measurement attributes under fuzziness. To do so, a methodology based on the Extent Fuzzy Analytic Hierarchy Process is proposed. Within the model, four main attributes such as Subject knowledge and achievements, Research aptitude, Personal qualities and strengths and Management skills with their subattributes are defined. The two approaches conventional AHP approach and the Extent Fuzzy Analytic Hierarchy Process approach have been compared on the same hierarchy structure and criteria set.

Keywords: AHP, Extent analysis method, Fuzzy AHP, Prioritization.

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Authors: C. Ardil

Abstract:

Aircraft supplier selection process, which is considered as a fundamental supply chain problem, is a multi-criteria group decision problem that has a significant impact on the performance of the entire supply chain. In practical situations are frequently incomplete and uncertain information, making it difficult for decision-makers to communicate their opinions on candidates with precise and definite values. To solve the aircraft supplier selection problem in an environment of incomplete and uncertain information, proximity measure method is proposed. It uses determinate fuzzy numbers. The weights of each decision maker are equally predetermined and the entropic criteria weights are calculated using each decision maker's decision matrix. Additionally, determinate fuzzy numbers, it is proposed to use the weighted normalized Minkowski distance function and Hausdorff distance function to determine the ranking order patterns of alternatives. A numerical example for aircraft supplier selection is provided to further demonstrate the applicability, effectiveness, validity and rationality of the proposed method.

Keywords: Aircraft supplier selection, multiple criteria decision making, fuzzy sets, determinate fuzzy sets, intuitionistic fuzzy sets, proximity measure method, Minkowski distance function, Hausdorff distance function, PMM, MCDM

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Authors: S. Khosravi, A. Afshar, F. Barazandeh

Abstract:

In this study we propose a novel monitor hydraulic automatic gauge control (HAGC) system based on fuzzy feedforward controller. This is used in the development of cold rolling mill automation system to improve the quality of cold strip. According to features/ properties of entry steel strip like its average yield stress, width of strip, and desired exit thickness, this controller realizes the compensation for the exit thickness error. The traditional methods of adjusting the roller position, can-t tolerate the variance in the entry steel strip. The proposed method uses a mathematical model of the system together with the expert knowledge to perform this adjustment while minimizing the effect of the stated problem. In order to improve the speed of the controller in rejecting disturbances introduced by entry strip thickness variations, expert knowledge is added as a feed-forward term to the HAGC system. Simulation results for the application of the proposed controller to a real cold mill show that the exit strip quality is highly improved.

Keywords: Fuzzy feed-forward controller, monitor HAGC system, dynamic mathematical model, entry strip thickness deviation compensation

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

Abstract:

Events discrimination and decision maker in sport field are the subject of many interesting studies in computer vision and artificial intelligence. A large volume of research has been conducted for automatic semantic event detection and summarization of sports videos. Indeed the results of these researches have a very significant contribution, as well to television broadcasts as to the football teams, since the result of sporting event can be reflected on the economic field. In this paper, we propose a novel fuzzy sequential technique which lead to discriminate events and specify the technico-tactics on going the game, nor the fuzzy system or the sequential one, may be able to respond to the asked question, in fact fuzzy process is not sufficient, it does not respect the chronological order according the time of various events, similarly the sequential process needs flexibility about the parameters used in this study, it may affect a membership degree of each parameter on the one hand and respect the sequencing of events for each frame on the other hand. Indeed this technique describes special events such as dribbling, headings, short sprints, rapid acceleration or deceleration, turning, jumping, kicking, ball occupation, and tackling according velocity vectors of the two players and the ball direction.

Keywords: Sequential process, Event detection, Soccer videos analysis, Fuzzy process, Spatio-temporal parameters.

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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