Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification

A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature M_s from chemical composition, austenitizing temperature and time for low alloy steel. The degree of membership of each sample was calculated with fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Compared with the results obtained by empirical models based on the same data, the results by the fuzzy method showed good precision. The accuracy of the fuzzy model is almost 6 times higher than that of the best empirical model. The influence of alloying elements, austenitizing temperature and time on M_s was analyzed quantitatively by using the fuzzy model. It is shown that there exists a nonlinear relationship between the contents of alloying elements in steels and their M_s, and the effects of austenltizing temperature and time on M_s temperature cannot be neglected.     

Small unmanned helicopters attitude controller by an on line adaptive fuzzy control system

Since small unmanned helicopter flight attitude control process has strong timevarying characteristics and there are random disturbances, the conventional control methods with unchanged parameters are often unworkable. An online adaptive fuzzy control system (AFCS) was designed, in a way that does not depend on a process model of the plant or its approximation in the form of a Jacobian matrix. Neither is it necessary to know the desired response at each instant of time. AFCS implement a simultaneous online tuning of fuzzy rules and output scale of fuzzy control system. The two cascade controller design with an inner (attitude controller) and outer controller (navigation controller) of the small unmanned helicopter was proposed. At last, an attitude controller based on AFCS was implemented. The flight experiment showed that the proposed fuzzy logic controller provides quicker response, smaller overshoot, higher precision, robustness and adaptive ability. It satisfies the needs of autonomous flight.     

Intelligent Control on Hot Strip Coiling Temperature

A new intelligent control scheme for hot strip coiling temperature is presented. In this scheme, the prediction model of finishing temperature and the presetting model of main cooling zone are establish based on BP neural network, the feed-forward open-loop control model of main cooling zone is constructed based on T-S fuzzy neural network, a new improved structure of T-S fuzzy neural network is developed, and the feedback close-loop control model of precision cooling zone is obtained based on fuzzy control. The effectiveness of the proposed scheme has been demonstrated by computer simulation with a satisfactory result.     

Fuzzy Economic Order Quantity Inventory Models Without Backordering

In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent chance programming (DCP) model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level. Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization (PSO) algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm.     

A dynamic fuzzy clustering method based on genetic algorithm

A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.     

Robust direct adaptive fuzzy control for nonlinear MIMO systems

For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm.     

Signal frequency based self－tuning fuzzy controller for semi－active suspension system

A new kind of fuzzy control scheme, based on the identification of the signal‘ s main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method ad-justs the fuzzy controller to achieve the best isolation effect by analyzing the main frequency‘ s characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the op-timal damping ratio is the output. Simulation results indicated that the proposed control method is very effec-tive in isolating the vibration.     

Fuzzy Set-Based Risk Evaluation Model for Real Estate Projects

With the rapid development of residential real estate market, risk evaluation has been an important task in the process of project. This paper describes a risk evaluation method for residential real estate projects based on fuzzy set theory which uses linguistic variables and respective fuzzy numbers to evaluate the factors. The primary weights of factors and evaluation of alternatives are determined by applying linguistic variables and fuzzy numbers. The notion of Shapley value is used to determine the global value of each factor in accomplishing the overall objective of the risk evaluation process, so the primary weights are revised, thus the importance of factors can be reflected more precisely. A major advantage of the method is that it allows experts and engineers to express their opinions on project risk evaluation in linguistic variables rather than crisp values. An illustration is presented to demonstrate the application of the method in risk evaluation. The results are consistent with the results calculated by conventional risk evaluation method. The research demonstrates that the method is objective and accurate, and is of an application value in the risk evaluation for residential real estate project.     

Color Image Enhancement Based on Maximum Fuzzy Entropy

A color image enhancement approach based on maximum fuzzy entropy and genetic algorithm is proposed in this paper. It enhances color images by stretching the contrast of S and I components respectively in the HSI color representation. The image is transformed from the property domain to the fuzzy domain with S-function. To preserve as much information as possible in the fuzzy the domain, the fuzzy entropy function is used as objective function in a genetic algorithm to optimize three parameters of the S-function. The Sigmoid function is applied to intensify the membership values and the results are transformed back to the property domain to produce the enhanced image. Experiments show the effectiveness of the approach.     

An Intelligent Adaptive Fuzzy PID Controller Based on Multimodel Control Approach

A novel intelligent adaptive fuzzy PHD controller based on multimodel control approach is presented in this paper.It can improve the system performance of the dynamic time- varying system at various operating conditions.The fuzzy PHD controller is implemented by combining a fuzzy PI with a fuzzy PD controller in a parallel structure. The parameters of the fuzzy PHD controller are linked, via analytical derivation, to the gains of the linear PID controller. The sum of error square is used as performance criterion to locate the model that best reresents the process among the multiple models, The desired control output to drive the process along the desired path is generated only by modifying the output scale factots GU_I and GU_D of the fuzzy PID controller, Among the prescribed models, the control signal of the nearestmmodel to the system is applied. The system can be driven to its original trajectory because of the robustness of the fuzzy PID controller, Computer simulation results show that the adaptiv     

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. the multiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learning algorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzy neural network model, the network is used to Predict short-term load by compaing the network output against the real load data from a local power system supplying electricity to a large steel manufacturer. The experimental results are satisfactory.     

Data fusion based on fuzzy measures

Choquet integral based on fuzzy measure is a very popular data fusion approach.A major problem in applying the Cho- quet integral is how to determine a large number of fuzzy measures as the number of attributes increases.Theλ-fuzzy measure proposed by Sngeno is a powerful method to resolve this problem.However,the modeling ability of theλ-fuzzy measure is too limited to satisfy actual requirements.In this paper,an extendedλ-fuzzy measure is proposed using Shapley value index,and the limitation of theλ-fuzzy measure is significantly overcome under little additional computational loads.The extended fuzzy measure has stronger modeling power than theλ- fuzzy measure,straightforwardly representing interaction among attributes.We apply the extended fuzzy measure to an artificial data set and a real dataset in an iron-steel plant.The results verify the usefulness of the extended fuzzy measure compared with other main existing methods.     

Data fusion based on fuzzy measures

Choquet integral based on fuzzy measure is a very popular data fusion approach. A major problem in applying the Choquet integral is how to determine a large number of fuzzy measures as the number of attributes increases. The λ-fuzzy measure proposed by Sugeno is a powerful method to resolve this problem. However, the modeling ability of the λ-fuzzy measure is too limited to satisfy actual requirements. In this paper, an extended λ-fuzzy measure is proposed using Shapley value index, and the limitation of the λ-fuzzy measure is significantly overcome under little additional computational loads. The extended fuzzy measure has stronger modeling power than the λ-fuzzy measure, straightforwardly representing interaction among attributes. We apply the extended fuzzy measure to an artificial data set and a real dataset in an iron-steel plant. The results verify the usefulness of the extended fuzzy measure compared with other main existing methods.     

Robust Controller Design of a Class of Uncertain Switched Fuzzy Systems with Delays

The robust control problem for a class of uncertain switched fuzzy systems with delays is investigated. Firstly,the model of the switched fuzzy system is presented and the parallel distributed compensation（ PDC） technology is employed to design fuzzy controllers. Then, based on the convex combination method, a sufficient condition for robust stabilization in terms of linear matrix inequalities（ LMIs） is obtained and a switching law is presented.Meanwhile,the Lyapunov-Krasovskii functional is taken to deal with time varying delays. Moreover,an algorithm is applied to finding a solution for a group of convex combination coefficient. Finally,a numerical example is given to demonstrate the effectiveness of the proposed method.     

Fuzzy fault diagnosis system of MCFC

A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.     

Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multifault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.     

Reliable fuzzy control with domain guaranteed cost for fuzzy systems with actuator failures

The reliable fuzzy control with guaranteed cost for T-S fuzzy systems with actuator failure is proposed in this paper. The cost function is a quadratic function with failure input. When the initial state of such systems is known, a design method of the reliable fuzzy controller with reliable guaranteed cost is presented, and the formula of the guaranteed cost is established. When the initial state of such systems is unknown but belongs to a known bounded closed domain, a notion of the reliable domain guaranteed cost (RDGC) for such systems is proposed. For two classes of initial state domain, polygon domain and ellipsoid domain, some design methods for reliable fuzzy controllers with the RDGC are provided. The efficiency of our design methods is finally verified by numerical design and simulation on the Rossler chaotic system.     

Fuzzy image restoration using modulation transfer function①

The key difficulty of restoring a fuzzy image is to estimate its point spread function ( PSF) .In the paper , PSF is modelled based on modulation transfer function ( MTF) .The first step is calculat-ing the image MTF.In the traditional slanted-edge method, a sub-block is always manually extrac-ted from original image and its MTF will be viewed as the result of the whole image .However , hand-craft extraction is inefficient and will lead to inaccurate results .Given this, an automatic MTF com-putation algorithm is proposed , which extracts and screens out all the effective sub-blocks and calcu-lates their average MTF as the final result .Then, a two-dimensional MTF restoration model is con-structed by multiplying the horizontal and vertical MTF , and it is combined with conventional image restoration methods to restore fuzzy image .Experimental results indicate the proposed method imple-mentes a fast and accurate MTF computation and the MTF model improves the performance of con-ventional restoration methods significantly .     

Stabilization of CSTR with Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control （SMC） theory is proposed to stabilize the nonlinear continuous stirred tank reactor （CSTR）.T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.