Fuzzy Estimation Research Articles

Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part II: Above Ground Biomass Estimation

Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.

Fuzzy estimation for process loss assessment

The process loss index was developed for providing a unitless measure of process performance by considering the proximity of the target value. It is defined as the ratio of the expected quadratic loss to the square of half the specification width. Using for measuring process performance, it also provides an uncontaminated separation between information concerning the process relative off-target loss and the process relative inconsistency loss. Most studies on measuring process loss index are based on crisp estimates involving precise output process measurements. However, measurements of product quality sometimes cannot be precisely recorded or collected. By combining randomness and fuzziness in assessing process capability, this study provides a triangular-shaped fuzzy number for the estimation of . The fuzzy upper confidence bound, derived by extension principle, can be used for estimating the maximum process loss. Finally, the decision rule for fuzzy hypothesis testing is presented and illustrated with an example for assessing process loss in the fuzzy environment.

MODELING QUEUEING SYSTEMS USING FUZZY ESTIMATORS

Queuing modeling is the mathematical approach for the analysis of waiting lines. The central problem in every queueing model is a trade-off decision: The manager must weigh the added cost of providing more rapid service against the inherent cost of waiting. In this paper, a new fuzzy approach to queueing modeling is presented in order to eliminate the disadvantages of point estimation and the relevant paradoxes when calculating the efficiency of a queue, in the case of unreliable data available. Both Poisson arrival rate and exponential service time are considered by using fuzzy estimators. The introduction of fuzzy estimators in performance measures of M/M/S queueing systems is presented to address the central estimation issue under uncertainty.

A Suggestion of Fuzzy Estimation Technique for Uncertainty Estimation of Linear Time Invariant System Based on Kalman Filter

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

State Estimate-Based Robust Adaptive Control of a Linked Mass-Spring System With Nonlinear Friction

This paper aims to develop a state estimate-based friction fuzzy modeling and robust adaptive control techniques for controlling a class of multiple degrees of freedom (MDOF) mechanical systems. A fuzzy state estimator is proposed to estimate the state variables for friction modeling. Under some conditions, it is shown that such a state estimator guarantees the uniformly ultimate boundedness (UUB) of the estimate error. Based on system input–output data and our proposed state estimator, a robust adaptive fuzzy output-feedback control scheme is presented to control multiple degrees of freedom system with friction. The adaptive fuzzy output-feedback controller can guarantee the uniformly ultimate boundedness of the tracking error of the closed-loop system. A typical mass-spring system is employed in our simulation studies. The results demonstrate that our proposed techniques in this paper have good potential in controlling nonlinear systems with uncertain friction.

Control Design of Uncertain Quantum Systems With Fuzzy Estimators

An approach of control design using fuzzy estimators (FEs) is proposed for quantum systems with uncertainties. Two types of quantum control problems are considered: 1) control of a pure-state quantum system in the presence of uncertainties and 2) control design of quantum systems with initial mixed states and uncertainties. For the first type of tasks, a partial feedback control scheme with an FE is presented to design controllers. In this scheme, an FE is trained to estimate the quantum state for feedback control of a quantum system, and controlled projective measurement is used to assist in controlling the system. For the second type of quantum control tasks, a probabilistic fuzzy estimator (PFE) is trained to estimate the quantum state for control design of a quantum system with an initial mixed state, and a corresponding control algorithm is proposed to design a control law that drives the system from the mixed state to a target pure state. Two examples of two-spin-1/2 systems are also presented and analyzed to demonstrate the process of control design and potential applications of the proposed approach.

Triangular and Trapezoidal Fuzzy State Estimation with Uncertainty on Measurements

In this paper, a new method for uncertainty analysis in fuzzy state estimation is proposed. The uncertainty is expressed in measurements. Uncertainties in measurements are modelled with different fuzzy membership functions (triangular and trapezoidal). To find the fuzzy distribution of any state variable, the problem is formulated as a constrained linear programming (LP) optimization. The viability of the proposed method would be verified with the ones obtained from the weighted least squares (WLS) and the fuzzy state estimation (FSE) in the 6-bus system and in the IEEE-14 and 30 bus system.

A new adaptive fuzzy sliding mode observer for a class of MIMO nonlinear systems

In this paper, a new adaptive fuzzy sliding mode (AFSM) observer is proposed which can be used for a class of MIMO nonlinear systems. In the proposed algorithm, the zero-input dynamics of the plant could be unknown. In this method, a fuzzy system is designed to estimate the nonlinear behavior of the observer. The output of fuzzy rules are tuned adaptively, based on the observer error. The output connection matrix is used to combine the observer errors of individual subsystems. A robust term, which is designed based on the sliding mode theory, is added to the observer to compensate the fuzzy estimation error. The estimation error bound is adjusted by an adaptive law. The main advantage of the proposed observer is that, unlike many of the previous works, the measured outputs is not limited to the first entries of a canonical-form state vector. The proposed observer estimates the closed-loop state tracking error asymptotically, provided that the output gain matrix includes Hurwitz coefficients. The chattering is eliminated by using boundary layers around the sliding surfaces and the observer convergence is proved using a Lyapunov-based approach. The proposed method is applied on a real multilink robot manipulator. The performance of the observer shows its effectiveness in the real world.

PH, electric conductivity and sulfate as base parameters to estimate the concentration of metals in AMD using a fuzzy inference system

An adaptive neuro-fuzzy inference system (ANFIS) was developed to model an acid mine drainage (AMD) affected river. The inference system uses three AMD relatively expeditious indicators (pH, electric conductivity and sulfate) and gives, as response, concentration values for Fe, Mn, Cu, Zn, Cd, and As. It was based on a large database, which resulted from monitoring an AMD stream located in the Iberian Pyrite Belt (Chorrito stream, which flows into the Cobica River, SW Spain). The results indicate a high correlation between the analyzed values and the concentrations obtained by fuzzy estimation. Therefore, this modeling approach afforded a simple and cost-effective system capable of monitoring the affected river, since it avoids or simplifies the analyses of the most expensive and time consuming chemical parameters.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.

Beam-rotating machinery system active vibration control using a fuzzy input estimation method and LQG control technique combination

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.

Reliability Estimation Under The Fuzzy Environments

This paper proposes new methods for fuzzy reliability estimation where lifetime random variables have a distribution function with fuzzy parameter. First, a fuzzy estimation is constructed for the parameter. Then, using such a fuzzy estimation, a fuzzy reliability estimation is constructed. In first method, we used Buckley method with precise observation. In secound one we uesd fuzzy point estimation with fuzzy observation. In third method, using a fuzzy estimation of mean time to failure (MTTF), we constructed a fuzzy confidence bound and a fuzzy confidence band for reliability function for a given \(t_0\) and for a given \(\alpha_0\) respectively. This method has been used in lifetime distributions as fuzzy normal distribution, fuzzy exponential distribution and fuzzy Weibull distribution.

A fuzzy clustering-based binary threshold bispectrum estimation approach

A fuzzy clustering bispectrum estimation approach is proposed in this paper and applied on the rolling element bearing fault recognition. The method combines the basic higher order spectrum theory and fuzzy clustering technique in data mining. At first, all the bispectrum estimation results of the training samples and test samples are taken binarization threshold processing and turned into binary feature images. Then, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples’ binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. Higher Order Cumulants, which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, are introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. At last, the rolling bearing fault diagnosis experiment results showed that the classification was completely correct.

Real time DDoS detection using fuzzy estimators

We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window.

Improved observer-based fault estimator design for a class of T-S fuzzy systems

For a class of Takagi–Sugeno (T-S) fuzzy nonlinear systems, in this article, an improved fuzzy observer-based fault estimator is designed to estimate the fault signals on-line based on common Lyapunov function. Attention is focused on the linear matrix inequality (LMI)-based design method with specified performance constraints and less conservatism, which is suitable for complex system models with more rules. The multiobjective and H ∞ optimisation theory is applied to cope with the constraints on the disc-regional poles assignment index and the robustness to disturbances. Some developed relaxing techniques are utilised to reduce the conservatism and the number of LMI constraints which were generated by the conventional design method with common quadratic Lyapunov function. Thus, the resulting estimator is not only less conservative, but can also guarantee the desired fault estimation performance on the rapidity as well as robustness to neglected modelling dynamics, and the robustness against external disturbances with satisfied estimation accuracy to faults signals. The conventional design is also presented for comparative study. All the results are formulated in the form of LMIs. Simulative examples demonstrate that our proposed method is less conservative and effective for fault estimation.

Hybrid (fuzzy-stochastic) modelling in construction operations management

In this work we apply hybrid (fuzzy-stochastic) modelling in construction operations management (COM). One of the most prevailing methods for the COM is the Program (or Project) evaluation review technique, mostly known as PERT. Nevertheless, both classic and fuzzy PERT presents evident drawbacks. Such drawbacks are the subjectivity of estimates for activity durations, the specification to a certain beta distribution and the assumption for applicability of this probability distribution to all project activities. Regarding the fuzzy PERT, a major drawback is the arbitrary choice of fuzzy numbers for the activity duration. In an attempt to reduce these drawbacks and reach more realistic results we propose an alternative hybrid (fuzzy-stochastic) approach. By combining the generalized beta distribution and the fuzzy estimators method, we find the fuzzy activity times (earliest start/finish—latest start/finish) of a project network and also a fuzzy probability for an activity’s duration to be in a certain time interval. A numerical example is given for thorough comprehension of the proposed method.

Fuzzy Testing for One-Sided Process Capability Indices

Process capability indices are widely used to evaluate the performance of processes in the manufacturing industry. Over the years, the issues have been investigated extensively. Some articles have studied them with fuzzy estimation. However, it seems that no article has proposed a version of triangular fuzzy numbers for critical value to test the process capability. In this article, we use Buckley's approach (2003) to construct the triangular fuzzy numbers for C pl and C pu , especially, the triangular fuzzy numbers for critical values are derived to execute the fuzzy hypothesis testing for C pl and C pu . Some numerical examples are taken to illustrate the proposed methodology.

Life time estimation of SSCs for decommissioning safety of nuclear facilities

This paper suggests the estimation algorithm for life time of structure, system and components (SSCs) for decommissioning safety of nuclear facilities using the performance data of linguistic languages and fuzzy theory. The fuzzy estimation algorithm of life time can be easily applicable but the estimated results depend on the relevant membership functions and performance characteristic functions. This method will be expected to be very useful for maintenance and decommissioning of nuclear facilities’ SSCs as a safety assessment tool.

Inverse active vibration force inputs estimation for a beam–machine system

This study presents an innovative fuzzy estimator that efficiently and robustly estimates the vibratory forces of a beam–machine system. The proposed estimator includes fuzzy Kalman filter technology, which is accelerated by the fuzzy accelerating factor and the fuzzy weighting recursive least-square method, weighted by the fuzzy weighting factor based on the fuzzy logic inference system. The excellent performance of this estimator is demonstrated by comparing it with different weighting functions, distinct levels of noise covariance measurement and the initial process noise covariance. The simulation results show that the proposed method is efficient in estimating input vibration forces, giving this method great stability and precision.

Presentation of a fast solution for solving HIV-infection dynamics and chemotherapy optimization based on fuzzy: AVK method

The issue of including the most optimal amount of medicine consumption in the program of illness control program is important in the control of HIV because of its high cost and high risk of side effects (poisoning), so applying a mathematical model and modern technique are necessary in solving optimal control problem for prescribing optimal amount. In this paper two ordinary differential equations systems which models interaction between HIV viruses and human body immune system is used. In addition, A. V. Kamyad (AVK) discretization method is introduced and is used to solve the mathematical models. The results are then extended to the other domain points of the mathematical model by a fuzzy inference estimator. Key words: A. V. Kamyad (AVK) method, fuzzy approximator, optimal control, HIV-infection dynamics.