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A variable structure control (VSC) scheme for linear black-box multi-input/multi-output (MIMO) systems based on support vector machine (SVM) is developed. After analyzing character of MIMO system, an additional control is designed to track trajectory. Then VSC algorithm is adopted to eliminate the difference. By estimating outputs of next step, VSC inputs and additional inputs are obtained directly by two kinds of trained SVMs, and so recognition of system parameters is avoided. A linear MIMO system is introduced to prove the scheme, and simulation shows that the high identification precision and quick training speed. [ABSTRACT FROM AUTHOR]

Based on fast online support vector regression (SVR) algorithm, reverse model of system model is constructed, and adaptive internal model controller is developed. First, SVR model and its online training algorithm are introduced. A kernel cache method is used to accelerate the online training algorithm, which makes it suitable for real-time control application. Then it is used in internal model control (IMC) for online constructing internal model and designing the internal model controller. Output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Last, the fast online SVM based adaptive internal model control was used to control a benchmark nonlinear system. Simulation results show that the controller has simple structure, good control performance and robustness. [ABSTRACT FROM AUTHOR]

In this paper, a design procedure of support vector machine (SVM) with RBF kernel function based internal model control (IMC) strategy for stable nonlinear systems with input-output form is proposed. The control scheme consists of two controllers: a SVM based controller which fulfils the direct inverse model control and a traditional controller which fulfils the close-loop control. And so the scheme can deal with the errors between the process and the SVM based internal model generated by model mismatch and additional disturbance. Simulations are given to illustrate the proposed design procedure and the properties of the SVM based internal model control scheme for unknown nonlinear systems with time delay. [ABSTRACT FROM AUTHOR]

Support Vector Machines are finding application in pattern recognition, regression estimation, and operator inversion. To extend the using range, people have always been trying their best in finding online algorithms. But the Support Vector Machines are sensitive only to the extreme values and not to the distribution of the whole data. Ordinary algorithm can not predict which value will be sensitive and has to deal with all the data once. This paper introduces an algorithm that selects promising vectors from given vectors. Whenever a new vector is added to the training data set, unnecessary vectors are found and deleted. So we could easily get an online algorithm. We give the reason we delete unnecessary vectors, provide the computing method to find them. At last, we provide an example to illustrate the validity of algorithm. [ABSTRACT FROM AUTHOR]

We introduce the support vector machine adaptive control by Lyapunov function derivative estimation. The support vector machine is trained by Kalman filter. Support vector machine is used to estimate the Lyapunov function derivative for affine nonlinear system, whose nonlinearities are assumed to be unknown. In order to demonstrate the availability of this new method of Lyapunov function derivative estimation, a simple example is given in the form of affine nonlinear system. The result of simulation demonstrates that the sequential training algorithm of support vector machine is effective and support vector machine control can achieve a satisfactory performance. [ABSTRACT FROM AUTHOR]

Most control applications closed over a shared network are suffering from the time-varying characteristics of flexible network workload. This gives rise to non-deterministic availability of communication resources and may significantly impact the control performance. In the context of integrating control and scheduling, a novel feedback scheduler based on neural networks is suggested. With a modular architecture, the proposed feedback scheduler mainly consists of a monitor, a predictor, a regulator and an actuator. An online learning Elman neural network is employed to predict the network conditions, and then the control period is dynamically adjusted in response to estimated available network utilization. A fast algorithm for period regulation is employed. Preliminary simulation results show that the proposed feedback scheduler is effective in managing workload variations and can provide runtime flexibility to networked control applications. [ABSTRACT FROM AUTHOR]

Based on KKT condition and Lagrangian multiplier method a weighted SVM regression model and its on-line training algorithm are developed. Standard SVM regression model processes every sample equally with the same error requirement, which is not suitable in the case that different sample has different contribution to the construction of the regression model. In the new weighted model, every training sample is given a weight coefficient to reflect the difference among samples. Moreover, standard online training algorithm couldn't remove redundant samples effectively. A new method is presented to remove the redundant samples. Simulation with a benchmark problem shows that the new algorithm can quickly and accurately approximate nonlinear and time-varying functions with less computer memory needed. [ABSTRACT FROM AUTHOR]

In this paper, a support vector machine (SVM) with linear kernel function based nonparametric model identification and dynamic matrix control (SVM_DMC) technique is presented. First, a step response model involving manipulated variables is obtained via system identification by SVM with linear kernel function according to random test data or manufacturing data. Second, an explicit control law of a receding horizon quadric objective is gotten through the predictive control mechanism. Final, the approach is illustrated by a simulation of a system with dead time delay. The results show that SVM_DMC technique has good performance in predictive control with good capability in keeping reference trajectory. [ABSTRACT FROM AUTHOR]

An approach for short-term load forecasting by combining self-organizing map(SOM) and support vector machine(SVM) is proposed in this paper. First, historical load data of same type are clustered using SOM, and then daily 48-point load values are vertically predicted respectively based on SVM. In clustering, factors such as date type, weather conditions and time delay are considered. In addition, influences of kernel function and SVM parameters on load forecasting are discussed and performance of SOM-SVM is compared with pure SVM. It is shown that normal smoothing technique in preprocessing is not suitable to be used in vertical forecasting. Finally, the approach is tested by data from EUNITE network, and results show that the approach runs with high speed and good accuracy. [ABSTRACT FROM AUTHOR]

A multitude of applications of wireless sensor networks require that the sensor nodes be location-aware. Range-based localization schemes are sometimes not feasible due to hardware cost and resource restriction of the sensor nodes. As cost-efficient solutions, range-free localization schemes are more attractive for large-scale networks. This paper presents Weighted Centriod (W-Centriod), a novel range-free localization scheme extended on the basis of Centroid scheme, which takes received signal strength indicator (RSSI) metric into account besides connectivity metric used in Centroid scheme. It's shown that our W-Centriod method outperforms Centriod scheme significantly in terms of both the average localization error and the uniformity of error distribution across different positions, which decrease by 49.3% and by 37.7%, respectively, under the best circumstance. Moreover, a two-phase localization approach consisting of a field data collection phase and an off-line parameter optimization phase is proposed for localization in wireless sensor networks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scalable energy efficient medium access control protocol (SEMAC) based on time division multiple access (TDMA) technique for wireless sensor networks (WSNs), which uses the local information in scheduling, eliminates most collisions, is more energy efficient and is scalable to the number of sensor nodes in WSN. SEMAC uses the concept of periodic listen and sleep in order to avoid idle listening and overhearing. To balance the energy used in the whole network, SEMAC lets the node with lower energy be a winner in an election procedure based on their energy levels and the winner has more chances to sleep to save energy. We also use a clustering algorithm to form clusters so as to increase the scalability of SEMAC. The performance of SEMAC is evaluated by simulations, and the results show the gain in energy efficiency and scalability. [ABSTRACT FROM AUTHOR]

The case where multiple control tasks share one embedded CPU is considered. For various reasons, both execution times of these tasks and CPU workload are uncertain and imprecise. To attack this issue, a fuzzy logic based feedback scheduling approach is suggested. The sampling periods of control tasks are periodically adjusted with respect to uncertain resource availability. A simple period rescaling algorithm is employed, and the available CPU resource is dynamically allocated in an intelligent fashion. Thanks to the inherent capacity of fuzzy logic to formalize control algorithms that can tolerate imprecision and uncertainty, the proposed approach provides runtime flexibility to quality of control (QoC) management. Preliminary simulations highlight the benefits of the fuzzy logic based feedback scheduler. [ABSTRACT FROM AUTHOR]

Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by a convex optimization problem, which involves the determination of a few additional tuning parameters. Moreover, the model complexity follows from that of this convex optimization problem. In this paper we introduce the support vector machine adaptive control by Lyapunov function derivative estimation. The support vector machine is trained by particle filter. The support vector machine is applied to estimate the Lyapunov function derivative for affine nonlinear system, whose nonlinearities are assumed to be unknown. In order to demonstrate the availability of this new method of Lyapunov function derivative estimation, we give a simple example in the form of affine nonlinear system. The result of simulation demonstrates that the sequential training algorithm of support vector machine is effective and support vector machine adaptive control can achieve a satisfactory performance. [ABSTRACT FROM AUTHOR]

Extended Continuous Event Graphs (ECEG) are a special class of Continuous Petri Nets. As the limiting form of Extended Timed Event Graphs (ETEG), ECEGs can be used not only to describe the discrete events approximately, but also to describe the continuous processes. In this note, we obtain some of the global properties of ECEGs. In the end, a simple example is given to illustrate the feedback control of CEGs with input. [ABSTRACT FROM AUTHOR]

The problem of neural network based iterative learning control (NNILC) in a resource-constrained environment with workload uncertainty is examined from the real-time implementation perspective. Thanks to the iterative nature of the NNILC algorithm, it is possible to abort the optimization routine before it reaches the optimum. Taking into account the impact of resource constraints, a feedback scheduling approach is suggested, with the primary goal of maximize the control performance. The execution time of the NNILC task is dynamically adjusted to achieve a desired CPU utilization level. Thus a tradeoff is done between the available CPU time and the control performance. For the sake of easy implementation, a practical solution with a dynamic iteration stop criterion is proposed. Preliminary simulation results argue that the proposed approach is efficient and delivers better performance in the face of workload variations than the traditional NNILC algorithm. [ABSTRACT FROM AUTHOR]

Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by a convex optimization problem, which involves the determination of a few additional tuning parameters. Moreover, the model complexity follows from that of this convex optimization problem. In this paper we introduce the sequential support vector machine for the regression estimation. The support vector machine is trained by the Kalman filter and particle filter respectively and then we design a controller based on the sequential support vector machine. Support vector machine controller is designed in the state feedback control of nonaffine nonlinear systems. The results of simulation demonstrate that the sequential training algorithms of support vector machine are effective and sequential support vector machine controller can achieve a satisfactory performance. [ABSTRACT FROM AUTHOR]