Volume 9, Number 6, 2012
The study on artificial intelligence (AI) methods for tuning of particle accelerators has been reported in many literatures. This paper presents tuning method for agent-based control systems of transport lines in the case of sensor/actuator failures. The method uses model-based tracking concept to relax the demand on sensor data. The condition for successful operation of the stated scheme is derived, and the concept is demonstrated through simulation by applying it to the model of microtron, transport line-1 and booster of indus accelerator. The results show that this approach is very effective in transport line control during sensor/actuator failures.
A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper. The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector. The gradient algorithm is used to deal with the identification problem. The effectiveness of this method is illustrated through simulation.
This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction. By employing the parameter separation technique and signal replacement mechanism, the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems. By incorporating a Nussbaum-type function, the proposed approach can deal with the unknown control direction of the nonlinear systems. Based on a Lyapunov-Krasovskii-like composite energy function, the convergence of tracking error sequence is achieved in the iteration domain. Finally, two simulation examples are provided to illustrate the feasibility of the proposed control method.
There are many kinds and a large number of raw materials in the sintering material ground to be managed, while it is difficult to obtain the precise inventory values, which often leads to high cost. Furthermore, the external factors of material ground are difficult to handle, such as weather variation, order fluctuation, measurement failure and so on. To solve such raw material management problems, a digital management system has been developed. First, the practical requirements and the raw material management processes are analyzed. Then, optimization and prediction methods are used to calculate the inventory according to the practical situation. With the help of practical technologies and production conditions, the developed system has been applied to a large-scale sintering material ground. The practical running results of the application demonstrate the validity of the proposed digital management system.
Node localization is a fundamental problem in wireless sensor network. There are many existing algorithms to estimate the locations of the nodes. However, most of the methods did not consider the presence of obstacles. In practice, obstacles will lead to blockage and reflection of communication signals between sensor nodes. Therefore, the presence of obstacles will greatly affect the localization result. In this paper, we implement an obstacle-handling algorithm based on the localization tool developed by MIT, The experimental result shows that the enhanced algorithm can reduce the average distance error by up to 46 %, compared to the original algorithm.
In this paper, a generalized form of the symmetric Banzhaf value for cooperative fuzzy games with a coalition structure is proposed. Three axiomatic systems of the symmetric Banzhaf value are given by extending crisp case. Furthermore, we study the symmetric Banzhaf values for two special kinds of fuzzy games, which are called fuzzy games with multilinear extension form and a coalition structure, and fuzzy games with Choquet integral form and a coalition structure, respectively.
In the last decade, ranking units in data envelopment analysis (DEA) has become the interests of many DEA researchers and a variety of models were developed to rank units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. Also, some of these data may be known only in terms of ordinal relations. While the models developed in the past are interesting and meaningful, they didn't consider both undesirable and ordinal factors at the same time. In this research, we develop an evaluating model and a ranking model to overcome some deficiencies in the earlier models. This paper incorporates undesirable and ordinal data in DEA and discusses the efficiency evaluation and ranking of decision making units (DMUs) with undesirable and ordinal data. For this purpose, we transform the ordinal data into definite data, and then we consider each undesirable input and output as desirable output and input, respectively. Finally, an application that shows the capability of the proposed method is illustrated.
Recently, genetic algorithms (GAs) have been applied to multi-modal dynamic optimization (MDO). In this kind of optimization, an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum. Our fuzzy genetic sharing (FGS) approach is based on a novel genetic algorithm with dynamic niche sharing (GADNS). FGS finds the optimal solutions, while maintaining the diversity of the population. For this, FGS uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS. Second, a modified tournament selection is used to control selection pressure. Third, a novel mutation with an adaptive mutation rate is used to locate unexplored search areas. The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator (GDBG).
Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process. Due to the low accuracy and unstable performance of the traditional effluent quality measurements, we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions. Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms. Ensemble extreme learning machine models overcome variations in different trials of simulations for single model. Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance. The proposed method is verified with the data from an industrial wastewater treatment plant, located in Shenyang, China. Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square, neural network partial least square, single extreme learning machine and ensemble extreme learning machine model.
The output feedback stabilization is considered for a class of nonlinear time-delay systems with inverse dynamics in this paper. An appropriate state observer is constructed for the unmeasurable system states in order to realize the control objective. By adopting the backstepping and Lyapunov-Krasovskii functional methods, a systematic design procedure for a memoryless output feedback control law is presented. It is shown that the designed controller can make the closed-loop system globally asymptotically stable while keeping all signals bounded. An illustrative example is discussed to show the effectiveness of the proposed control strategy.