Earticle

Soft Computing tools are becoming very popular in solving hydrological problems. These tools have immense strength to deal with such complex problems. Water Table elevation estimation is an important aspect to understand the mechanism of ground water resources. The present study aims at the application of Artificial Neural Networks (ANN) & Fuzzy logic for simulation of water table elevation. This paper also investigates the best model to forecast water table elevation. Ten ANN models are developed in this study. These developed models are trained, tested and validated on the available data of Budaun District. Comparing observed data and the estimated data through developed ANN models and Fuzzy models, it has been observed that the developed Fuzzy models predict better results for four models and for model-5 ANN bore better results.

The robust 𝐻∞ control design for bilinear systems with multi inputs is presented in this paper. First, the bilinear system is represented as a dynamic Takagi-Sugeno (TS) fuzzy system by using sector nonlinearity approach. The dynamic TS fuzzy system is a convex combination of local linear systems. The local robust 𝐻∞ controller is designed for each local linear system. The controller synthesis for the local linear systems is then formulated in the bilinear matrix inequalities (BMIs) problem. After that, the BMIs problem is reduced to an equivalent parameter of linear matrix inequalities (LMIs) problem which has a feasible solution. The robust 𝐻∞ controller for bilinear systems as a convex combination of the local robust 𝐻∞ controllers is obtained by using defuzzyfication. The existence condition of the robust 𝐻∞ controller for the bilinear systems is also presented. The simulation results are given to clarify the proposed method for the robust 𝐻∞ control design of the bilinear systems.

The system of adaptability evaluation for national defense students is usually qualitative. In this paper the complexity of adaptive capacity and the influence to each other of various factors are considered, a two-stage quantitative index evaluation is proposed based on fuzzy model. The ideological political quality, comprehensive quality, professional quality, physical and psychological quality constitute the first-level indices. On the basis of these four indices, eighteen secondary-level indices are refined out. the first-level indicators can be generated through secondary-level indices by using membership function, the qualitative evaluation of adaptability evaluation for the national defense students can generated by the maximum membership degree principle in the first-level indicators. Finally, a concrete example is used to show the process of this scheme which is given in the paper and the feasibility of the method.

Automotive service enterprise location is an interesting and important issue in the logistic field. In practice, some factors of its facility location allocation (FLA) problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus FLA problem features with uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy time and cost issues for locating an automotive service enterprise. However, a decision-maker hopes to minimize the transportation time of customers meanwhile minimizing their transportation cost when locating a facility. Also, they prefer to arrive at the destination within the specific time and cost. To handle this issue via a more practical manner, by taking the vehicle inspection station as a typical automotive service enterprise example, this work presents a fuzzy multi-objective expected value optimization approach to address it. Moreover, some region constraints can greatly influence FLA and travel velocity is also an uncertain variable due to the influence of some unpredictable factors in the location process. To do so, this work builds two practical fuzzy multi-objectives programming models of its location with regional constraints, fuzzy inspection demand, and varying velocity. A hybrid algorithm integrating fuzzy simulation, neural networks (NN), and Genetic Algorithms (GA), namely a random weight based multi-objective NN-GA, is proposed to solve the proposed models. A numerical example is given to illustrate the proposed models and the effectiveness of the proposed algorithm.

Fuzzy logic model and entropy model are useful for the geohazard susceptibility zonation in Jianshi County of Qingjiang River Basin. In this paper, the same impact factors were chosen and the geohazard samples were considered in two cases with quantitative analysis method. The first case 162 geohazards chosen as samples and the other one all of 182 geohazards chose as samples. The authors completed the susceptibility zonation in the two different cases using the two models in order to analysis the effects of the two models. The results of the two models in different cases were almost the same in space, except small differences in some areas. The entropy model was more accurate for the analysis of relationship between impact factors and geohazards, but not stable for different geohazard samples. The fuzzy logic model was better for less geohazard samples. According to the analysis process, it was found that the fuzzy γ operation was the best which was defined in terms of the fuzzy algebraic product and the fuzzy algebraic sum. The results of fuzzy logic model were most useful when γ was 0.20. The fuzzy logic model and entropy model were useful for the geohazard susceptibility which was scientific and useful for the government to manage the geohazards and make the preliminary development plans.

Fuzzy Gaussian mixture modeling method is proposed in this paper for network anomaly detection. A mixture of Gaussian distributions was used to represent the network data in multi-dimensional feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the KDD Cup data set. Experimental results have shown that the proposed method is more effective than the vector quantization method.

In critical fields such as flight, agriculture, tourism, etc, forecasting is an important issue due to its effectiveness in human life to know what will happen for unpredictable situations and events. Weather forecasts provide critical information about future weather conditions. Actually, weather forecasting plays an important role in our daily life by predicting what the weather will be tomorrow and it is reflected in a wide area of applications in our life so, we can prevent huge damages by forecasting the coming of storms or typhoons or get benefits from the forecasting activities. The temperature warnings are important forecasts because they are used to protect life and property and to improve the efficiency of operations. We propose computer-based models for weather forecasting based on temperature to predict the daily temperature using two techniques, artificial neural networks and fuzzy logic. The main purpose from this study is to develop different weather forecasting models based on the two techniques over different regions. The developed models show that the objectives of the study were achieved successfully. Finally, the models have been tested and the results confirm that the proposed models are capable to forecast the daily temperatures.

Fuzzy sliding mode controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

In this paper, well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. Based on the fuzzy models, a numerical robust stability analysis for the fuzzy feedback linearization regulator is presented using Linear Matrix Inequalities (LMI) Theory. For these structured uncertainty, the closed system can be cast into Lur'e system by simple transformation. From the LMI stability condition for Lur'e system, we can derive the robust stability condition for the fuzzy feedback linearization regulator based on Takagi-Sugeno fuzzy model. The effectiveness of the proposed analysis is illustrated by a simple example.

This paper investigates the fuzzy control problem of a class of nonlinear continuous-time stochastic systems with achieving the passivity performance. A model-based observer feedback fuzzy control utilizing the concept of so-called parallel distributed compensation (PDC) is employed to stabilize the class of nonlinear stochastic systems that are represented by the Takagi-Sugeno (T-S) fuzzy models. Based on the Lyapunov criteria, the Linear Matrix Inequality (LMI) technique is used to synthesize the observer feedback fuzzy controller design such that the closed-loop system satisfies stability and passivity constraints, simultaneously. Finally, a numerical example is given to demonstrate the applicability and effectiveness of the proposed design method.

The purpose of this paper is to study the stability analysis and controller synthesis principles of Discrete Perturbed Time-Delay Affine (DPTDA) Takagi-Sugeno (T-S) fuzzy models. In general, the T-S fuzzy model is a weighted sum of some linear subsystems via fuzzy membership functions. This paper considers fuzzy rules include both linear nominal parts and uncertain parameters in the time-delay affine T-S fuzzy model. For DPTDA T-S fuzzy models, the T-S fuzzy control scheme is used to confront the $H_{\infty}$ performance constraints. Some sufficient conditions are derived on robust $H_{\infty}$ disturbance attenuation in which both robust stability and a prescribed performance are required to be achieved. In order to find suitable fuzzy controllers, the Iterative Linear Matrix Inequality (ILMI) algorithm is employed to solve these sufficient conditions. At last, a numerical simulation for the nonlinear truck-trailer system is given to show the applications of the present design approach.

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy models have recently been used to predict stock market prices because they are capable of extracting useful information from large sets of data without any assumption about a mathematical model. In this paper, three types of fuzzy rule formats to predict daily and weekly stock price indexes were presented. Their premises and consequences were composed of trapezoidal membership functions and novel nonlinear equations, respectively. As the most effective indicators for stock prediction, the information used in traditional candle stick-chart analysis was newly employed as input variables of our fuzzy models. The optimal fuzzy models were identified through an evolutionary process of differential evolution(DE). The different types of fuzzy models to predict the daily and weekly open, high, low, and close prices of the Korea Composite Stock Price Index (KOSPI) were built, and their performances were compared.

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

In this paper, multi-objective models for designing 3D trajectory of horizontal wells are developed in a fuzzy environment. Here, the objectives of minimizing the length of the trajectory and the error of entry target point are fuzzy in nature. Some parameters, such as initial value, end value, lower bound and upper bound of the curvature radius, tool-face angle and the arc length of each curve section, are also assumed to be vague and imprecise. The impreciseness in the above objectives have been expressed by fuzzy linear membership functions and that in the above parameters by triangular fuzzy numbers. Models have been solved by the fuzzy non-linear programming method based on Zimmermann [1] and Lee and Li [2]. Models are applied to practical design of the horizontal wells. Numerical results illustrate the accuracy and efficiency of the fuzzy models.

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control.

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.