Fuzzy Identification Research Articles

A neuron fuzzy identification system based on a complex nonlinear mathematical model

A neuron fuzzy identification system based on a complex nonlinear mathematical model

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

Research and application of node fuzzy identification and localization in wireless sensor networks

SummaryIn recent years, sensor technology has attracted the attention of countries all over the world because of its potential huge application value, and it has played an important role in people's daily life and social production activities. This paper mainly studies the location algorithm based on ranging in the location algorithm and analyzes the advantages and disadvantages of the algorithm. In order to optimize the positioning of wireless sensor networks, a fuzzy controller is designed to fuzzify the input information of sensor nodes. Using Matlab network simulation tool, the congestion control algorithm proposed in this paper was simulated and compared with several typical congestion control protocols in wireless sensor networks. Experimental results show that the algorithm can improve network throughput, reduce network packet loss, and have a significant impact on network performance. The network throughput of the congestion control algorithm based on fuzzy logs is stable at 180 packets per second. Compared with CODA, the congestion control algorithm based on fuzzy logarithm increases the packet loss rate by 55–66%, and the SenTCP algorithm increases by 37–40%. It can be seen that congestion control algorithms based on fuzzy logic can play an important role in wireless sensor networks.

A fuzzy logic risk control and self-assessment metrics for e-banking operational risk analysis

Operational risk is the risk of losses arising from the failure or inadequate internal processes, human resources, systems, and external events that affect the bank's operations as defined by Basel Committee on Banking Supervision. Defining a suitable set of risk measurement metrics is considered one of the most important issues for any risk analysis. It enables the quantitative evaluation of the risk exposure level and the effectiveness of internal control system. Risk measurement is needed to provide an effective means to quantify the risk of existing or planned systems to enable understanding of the overall security level and to guide decision making. Given the number of successful attacks against financial Institutions and the sophistication of the tactics used by attackers, existing classical measurement approaches are no longer enough. This study focuses on fuzzy logic-based metric identification to measurement of the risk exposure level, to enable financial institutions to see the overall risk level and security state of their E-banking systems and to assist with decision making. This will provide a newer dimension to risk management by shifting from risk measurement based on probability and classical set theory to Fuzzy Logic (FL) measurement. In this paper fuzzy logic-based metrics is presented and expressed as a function of six factors (triggering events, avoidance, recovery, Undesirable Operational State (UOS), cost of Undesirable Operational State (UOS) occurrence and severity of risk occurrence) as proposed by [1].

Analysis on Distributed Output Regulator of Discrete Multi-agent System Combined with Fuzzy Identification Method

This paper combines the traditional output regulation (OR) theory with fuzzy identification method, and designs a distributed output regulator for the multi-agent system (MAS). In view of the situation that the follower model in the MAS cannot be obtained accurately, the fuzzy model is established to make it approximate to the considered nonlinear discrete follower agent system first. By employing fuzzy identification method and OR theory, a distributed controller is proposed, make follower agents track the reference signal given by the dynamic leader. Two numerical examples demonstrate the obtained results.

Quantitative evaluation of industrial and commercial management’s promotion to economic growth based on hierarchical constraints

In order to improve the quantitative evaluation ability of industrial and commercial management on promoting economic growth, the design method of quantitative evaluation model of industrial and commercial management on promoting economic growth based on hierarchical constraints is proposed. the descriptive statistical analysis method to realize big data fusion mining for quantitative evaluation of business administration’s promotion to economic growth is used, and a hierarchical constraint parameter analysis model to realize node deployment model design of hierarchical constraint parameter analysis model is designed, fuzzy control and adaptive feature parameter identification are realized in the process of quantitative evaluation of industrial and commercial administration’s promotion to economic growth. Convergence control is realized in the process of quantitative evaluation based on pattern recognition and association distributed fusion. Fuzzy degree identification and optimization prediction of quantitative evaluation of industrial and commercial administration’s promotion to economic growth are realized in hierarchical constraint parameter analysis model. The simulation results show that the output accuracy and system stability of the quantitative evaluation of the promotion of business administration to economic growth are good.

Fuzzy identification of systems based on adaptive neurons

The identification of nonlinear systems is a complex task. This article presents a method comparison between the new Fuzzy Adaptive Neurons (FAN), Radial Basis Function Network (RBF), and Adaptive Network-Based Fuzzy Inference System (ANFIS). The nonlinear systems presented are solved with stable and optimal learning. The simulation of the results for two models presented, are carried out in Matlab®, the optimization of the system identification for the first and second systems were obtained with great success.

Cognitive Modeling of Socio-Economic Systems with Functional Consideration of the Time Factor

The research analyzes scientific and methodological approaches for structural and parametric identification of fuzzy cognitive models based on time-dependent functional dependencies. In the process of constructing and identifying fuzzy cognitive models, the stages of structural identification with the definition of a set of concepts and unclear relationships over this set, parametric identification, which implements the transition to fuzzy identification with the definition of the intensity of influence between factors, are implemented. A specialized software system was developed for the purpose of computer support for modeling and studying the influence of the time factor set by changes in the strength of connections between concepts. A representation of each of the elements of the FCM adjacency matrix is proposed in the form of an additive expression controlled by the alpha parameter, which includes a constant component determined by the expert method and a control function that depends on real or dimensionless model time. To correct the elements of the FCM adjacency matrix, it is proposed to use a 3-parameter parabolic or modified 4-parameter exponential dependence as a function that depends on real or dimensionless model time. The developed modified method for solving the problem of time factor accounting using functional dependencies for the strength of mutual influence of concepts provides for expanding the capabilities of fuzzy cognitive models by taking into account the time factor.

T-S fuzzy systems optimization identification based on FCM and PSO

The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

Entropy Fuzzy System Identification for the Heave Flight Dynamics of a Model-Scale Helicopter

This article studies nonlinear system identification of a small scale and flybar-free unmanned helicopter, the Trex450 chopper, built using commercial off-the-shelf components. We employ the real-time input-output data, obtained from human-controlled flight tests, operating the aircraft under severe ground effects during the vertical flight maneuvers. We highlight the efficacy of the entropy fuzzy system identification method with respect to the performance of several well-known nonlinear system identification techniques (i.e., a Takagi-Sugeno Fuzzy system, an adaptive neuro-fuzzy inference system (ANFIS), and a nonlinear autoregressive with exogenous (NARX) model) as our benchmarks. Our research confirms the benefits of the entropy fuzzy identification technique. Despite being nonlinear, the proposed fuzzy model is relatively simple, transparent, and highly accurate to represent the complex non-linear dynamic behaviors of our unmanned helicopter under severe ground effects. Another major advantage of the proposed system identification technique is its ability to avoid overfitting, an essential requirement in modeling. Overall, the fuzzy system is also capable of achieving a delicate balance between maximizing the accuracy while minimizing the complexity of the acquired model.

Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied. Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

A synthesis method for path generation of a planar five-bar mechanism based on dynamic self-adaptive atlas database

A method for the path synthesis of a planar five-bar mechanism based on wavelet analysis theory is proposed in this paper. Wavelet transform analysis is used to extract wavelet characteristic parameters(WCPs) from the basic dimensional types(BDTs) database. Combined the numerical atlas method with multidimensional search tree, a four dimensions search tree with a depth of five is obtained, in which numbers of leaf nodes are 4,096, and each node stores 59,787 groups of BDTs database numbers. Meanwhile, an index keyword database is established. By comparing the WCPs of a prescribed curve with index keywords, the leaf node where the desired mechanism is located is found. The WCPs of the BDTs in the leaf node are extracted, and a dynamic self-adaptive atlas database is established. Based on the design requirements, the desired mechanism can be obtained by fuzzy identification. Then, a mathematical model is established for calculating the actual sizes of the desired mechanism. The arbitrary design interval path synthesis of a five-bar mechanism is realized. Two examples are presented to demonstrate the veracity and practicality of the method proposed.

Prediction of forging dies wear with the modified Takagi–Sugeno fuzzy identification method

ABSTRACT Modern design of technological processes employs various numerical tools. Artificial Intelligence is one of the most promising approaches. However, its application in industrial conditions is still limited. One of the important obstacles is a lack of large data sets, which are necessary for the most of AI approaches. In the paper, we present a modified Takagi–Sugeno method, one of Fuzzy Rule-Based Systems family, applied for prediction of forging dies wear. A technological process, as well as acquired data are briefly discussed. The modified Takagi-Sugeno approach is introduced. Its main advantage, acquiring knowledge from experts instead of datasets, is emphasized. Assumptions, model details, and prediction results are included.

Single Pole-to-Ground Fault Analysis of MMC-HVDC Transmission Lines Based on Capacitive Fuzzy Identification Algorithm

The probability of a single pole-to-ground fault in high voltage direct current (HVDC) transmission lines is relatively high. For the modular multilevel converter HVDC (MMC-HVDC) systems, when a single pole-to-ground fault occurs, the fault current is small, and it is difficult to identify the fault quickly. Through a detailed analysis of the characteristics of the single pole-to-ground fault of the MMC-HVDC transmission line, it is found that the single pole-to-ground fault has obvious capacitance-related characteristics, and the transient process after the single pole-to-ground fault is the discharge process of the distributed capacitance of the line. However, other faults do not have such obvious capacitance-related characteristics. Based on such feature, this paper proposes a novel capacitive fuzzy identification method to identify the single pole-to-ground fault. This algorithm can effectively identify both the fault of single pole-to-ground and the fault pole, which can contribute to the large database of the future smart grid.

Fuzzy Identification of Nonlinear Dynamic System Based on Input Variable Selection and Particle Swarm Optimization Parameter Optimization

Input variable selection is an essential step in the development of data-driven models. In order to establish a fuzzy model with high identification accuracy for complex nonlinear systems (such as variable load pneumatic loading system) in engineering, a novel fuzzy identification method is proposed, which is based on the selection of important input variables. Firstly, the simplified Two Stage Fuzzy Curves and Surfaces method is used to rank the original input variables according to their significance, and the variables which are most relevant to the output are selected as the input of the T-S fuzzy model. Then, the Fuzzy c-Means clustering algorithm and Particle Swarm Optimization algorithm are used to identify the antecedent parameters, and the Recursive Least Square method is used to identify the consequent parameters. The validity of the proposed fuzzy identification method is verified by two benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach is implemented to the practical data of an actual variable load pneumatic loading system, and preponderant trajectory matching performance is achieved.

A fuzzy identification method for persistent scatterers in PSInSAR technology

Persistent Scatterer SAR Interferometry (PSInSAR) is known as one of the most effective technique for monitoring and analyzing ground deformation. It is a key step that how to identify Persistent Scatterers (PS) effectively and automatically from time-series SAR images. In the past research, one pixel will be classified to "PS" set or "no-PS" set clearly by one or more threshold rules for PS features. However, it is easy to fall into the 'either this or that' logical paradox in some cases because the covered objects in study area usually possess ambiguous boundary for interested characteristics. In this paper, a fuzzy PS concept is present and a fuzzy identification method is designed based on fuzzy set theory by taking the fuzzy characteristics of the pixels into account. Two groups of real data tests indicate that the new method can not only recognize more effective and reliable PS, but also can obtain the better quality results of selected PS, which can be used to evaluate the result of deformation and further improve the PSInSAR technology.

Fuzzy identification of dynamic loads in presence of structural epistemic uncertainties

The interval identification of dynamic loads for uncertain structures was performed primarily by the interval perturbation method (IPM). Influences of structural epistemic uncertainties on unknown loads cannot be effectively quantified for now and the interval inversion needs to be furtherly investigated. In this paper, Tikhonov regularized technique for the inverse analysis is integrated into a non-intrusive de/re-uncertainty procedure, i.e. the dimension-wise analysis, to reconstruct unknown loads under structural epistemic uncertainties. The proposed method transforms the interval identification model at zero-cut of fuzzy inputs into a finite number of classical ones with crisp parameters, regularized solutions to which are adopted to construct a polynomial approximation of each slice of the unknown load. The extreme point set of each approximation at zero-cut is derived to calculate its minimum/maximum (min/max) points at any alpha-cut with respect to the corresponding uncertain parameter, from which the min/max input vectors of the unknown load are assembled in a dimension-wise manner. Subsequently, the interval bounds of the unknown load at each alpha-cut can thus be evaluated by Tikhonov regularized inversion, based on which the fuzzy description is obtained. The effectiveness of the proposed method is validated by the inclusion of the unknown load in the identified result and a tighter identified interval suggests its accuracy advantage over the IPM if the same regularization method is adopted.

An anthropomorphic fuzzy model for the time-spatial assessment of sandstone seepage damage

Abstract Sandstone moisture is a significant cause of structural instability and efficient seepage damage identification is an inevitable problem for complicated construction engineering. To overcome the shortcomings of traditional rock moisture measurements in terms of real-time application, prior knowledge-dependence, etc., an anthropomorphic fuzzy model is proposed. In this model, an adaptive fuzzy identification algorithm and an anthropomorphic causal reasoning method are employed to conduct real-time moisture content assessment and damage depth level location, respectively. The experiment carried on a self-developed three-layer system shows the consistency of this model compared with measurement data and finite element simulation results. The historical acoustic emission (AE) data is fully used in this model, and time-spatial assessment of sandstone seepage damage can be achieved independent of complex construction engineering knowledge. In follow-up research, the probability information between AE parameters and seepage damage would be mined through more experiments and numerical simulations.

Two-Stage Optimal Operation Strategy of Isolated Microgrid With TSK Fuzzy Identification of Supply Security

Due to the uncertainty of intermittent energy and system load, it is a big challenge to optimally operate an isolated power system. This article proposes a two-stage optimal operation strategy with a Takagi–Sugeno–Kang (TSK) fuzzy system to address the supply security under uncertainty circumstance. For proper analysis of the uncertainty characteristics, adjustable uncertainty parameters of intermittent energy resource and system load are taken as fuzzy sets; with the consideration of the robustness of these uncertainty parameters on isolated power system, it creates a supply-security identification model with the TSK fuzzy approach under radial basis function (RBF) neural network, and deduces optimal weight values with a recursive least square method. For properly avoiding potential risks, security index is classified into several degrees, each degree of risk can switch a different operation model, which can ensure the supply security of an isolated power system. For properly solving the optimization model, gradient descent-based multiobjective cultural differential evolution is employed to minimize economic cost and emission rate simultaneously. With simulations on isolated regional network, the obtained results reveal that the proposed method can be a viable alternative for optimal operation in isolated power systems.