This paper presents a systematic methodology to the design of a multivariable fuzzy logic controller (MFLC) for large-scale nonlinear systems. A new general method which is based on a performance index of sliding moti...
详细信息
This paper presents a systematic methodology to the design of a multivariable fuzzy logic controller (MFLC) for large-scale nonlinear systems. A new general method which is based on a performance index of sliding motion is used to generate a fuzzy control rule base. Reducible input variables obtained from sliding motion are adopted as input variable of the fuzzy controller and the output scale factors of the MFLC are tuned by the switching variable. Thus, the determination of the input/output scale factors becomes easier and the system performance is significantly improved. The simulation results of a Puma 560 system and a two-inverted pendulum system demonstrate that the attractive features of this proposed approach include a smaller residual error and robustness against nonlinear interactions.
A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algo...
详细信息
A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, replacing the dynamic programming algorithm with a memoized recursive algorithm whose run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.
Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation -based, partial rescheduli...
详细信息
Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation -based, partial rescheduling method, combined with an existing multi -agent system, to swap jobs between machines. The negotiations are restricted to machines within the same work center, giving rise to a partial reschedule. A learning algorithm is also utilized, allowing machines to individually learn how to evaluate proposed bids from other machines and adapt the bids to their current environment. The main objective is to minimize the mean weighted tardiness of all jobs. Computational results indicate an improvement of 10-30 tardiness, compared to continuous rescheduling and complete rescheduling methods. In addition, a decrease of 70-80 sensitivity analysis and analysis of the partial reschedule.
The forecasting accuracy of downstream water levels greatly impacts the economic operation of reregulating hydropower stations. If present, large errors in water level forecasting may require the hydropower station to...
详细信息
The forecasting accuracy of downstream water levels greatly impacts the economic operation of reregulating hydropower stations. If present, large errors in water level forecasting may require the hydropower station to discard water, which decreases the revenue from hydropower generation. Especially when the reregulating hydropower station undertakes the task of peak load regulation of the power grid, its power output may change drastically over a short period of time. Such large changes in the discharged flow of the hydropower station may inevitably cause an unsteady flow, which can lead to large fluctuations in the water levels of downstream rivers. In this case, the current forecasting methods, such as standard rating curves and empirical formulae, may inevitably result in large deviations when forecasting the change in the downstream water level. In order to reduce the volume of water discarded as a result of forecasting errors, a back propagation neural network-based forecasting model for downstream water levels of a reregulating hydropower station was constructed. The model enabled the direct, accurate, real-time forecasting of changes in downstream water levels by using measurable operation data from a hydropower station and process data on downstream water level changes. This method was applied to the actual hydropower generation dispatch of the Gezhouba Hydropower Plant in China. The results showed that as the peak load regulation volume increased, the forecasting errors of two conventional methods (standard rating curves and empirical formulae) became continuously superimposed. However, the water level forecasting error of the neural network-based method was small and could fully meet the real-time dispatching requirements of the hydropower station. In particular, under large peak load regulations, the maximum absolute values of the forecasting errors of the two conventional methods were close to 1 m, while that of the neural network-based method could be c
The dual purpose principal and minor subspace gradient flow can be used to track principal subspace (PS) and if altered simply by the sign, it can also serve as a minor subspace (MS) trackor. This is of practical sign...
详细信息
The dual purpose principal and minor subspace gradient flow can be used to track principal subspace (PS) and if altered simply by the sign, it can also serve as a minor subspace (MS) trackor. This is of practical significance in the implementations of algorithms. In this paper, a unified information criterion is proposed and a dual purpose principal and minor subspace gradient flow is derived based on the information criterion. In this dual purpose gradient flow, the weight matrix length is self-stabilizing, i.e., moving towards unit length in each learning step. The energy function associated with the dual purpose gradient flow for tracking PS and MS is given, and it exhibits a unique global minimum attained if and only if its state matrices span the PS or MS of the autocorrelation matrix of a vector data stream. The other stationary points of its energy function are (unstable) saddle points. The proposed dual purpose gradient flow can efficiently track an orthonormal basis of the PS or MS, which is illustrated through simulation experiments.
Twin parametric-margin support vector machine (TPMSVM) obtains a significant performance. However, its decision function loses the sparsity, which causes the prediction speed to be much slow. In this brief, we present...
详细信息
Twin parametric-margin support vector machine (TPMSVM) obtains a significant performance. However, its decision function loses the sparsity, which causes the prediction speed to be much slow. In this brief, we present an improved TPMSVM, named centroid-based twin parametric-margin support vector machine (CTPSVM). The significant advantage of CTPSVM over twin support vector machine (TWSVM) and TPMSVM is that its decision hyperplane is sparse by optimizing simultaneously the projection values of the centroid points of two classes on its pair of nonparallel hyperplanes. In addition, a learning algorithm based on the clipping strategy is proposed to solve the optimization problems. Experimental results show the effectiveness of our method in speed, sparsity and accuracy, and therefore confirm further the above conclusion. (C) 2014 Elsevier B.V. All rights reserved.
A Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of appl...
详细信息
A Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of applications, including smart ports, delivery of products, construction, monitoring of the environment and climate, and military surveillance. FANETs research is being driven by the potential for UAVs to be utilized in these regions. The purpose of this paper is to provide a comprehensive analysis of the most important FANET characteristics, mobility models, applications, and routing protocols. The present paper is an effort to provide a comprehensive description of the various routing techniques utilized by the most prevalent routing protocols in FANETs, including topology-based, position- based, hierarchical, swarm-based, and Delay Tolerant Networking (DTN) protocols. Reinforcement learning and deep reinforcement learning are both encompassed in a newly anticipated classification. In the meanwhile, this study primarily centres around the taxonomy for learning agents (single- agent, multi-agent) and learning models (model-based and free-model). In addition, the paper intends to shed light on identifying the applications of FANETs in various categories and identify research gaps and future opportunities in this field. In addition, it compares the results qualitatively to those of the previous surveys. Any future work on the FANET routing protocol could benefit from this paper as a reference and roadmap.
Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface (BCI) based on electroencephalography (EEG). Many existing methods estimate the uncorrelated instantaneous de...
详细信息
Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface (BCI) based on electroencephalography (EEG). Many existing methods estimate the uncorrelated instantaneous demixing of EEG signals to classify multiclass motor imagery (MI). However, the condition of uncorrelation does not hold true in practice, because the brain regions work with partial or complete collaboration. This work proposes a novel method, termed as a common Bayesian network (CBN), to discriminate multiclass MI EEG signals. First, with the constraints of a Gaussian mixture model on every channel, only related channels are selected to construct a normal Bayesian network. Second, the nodes that have both common and varying edges are selected to construct a CBN. Third, the probabilities on common edges are used to learn about the support vector machine for classification. To validate the proposed method, we conduct experiments on two well-known BCI datasets and perform a numerical analysis of the propose algorithm for EEG classification in a multiclass MI BCI. Experimental results show that the proposed CBN method not only has excellent classification performance, but also is highly efficient. Hence, it is suitable for the cases where a system is required to respond within a second.
Variable selection, the process of identifying input variables that are relevant to a particular learning problem, has received much attention in the learning community. Methods that employ a learning algorithm as a p...
详细信息
Variable selection, the process of identifying input variables that are relevant to a particular learning problem, has received much attention in the learning community. Methods that employ a learning algorithm as a part of the selection process ( wrappers) have been shown to outperform methods that select variables independently from the learning algorithm ( filters), but only at great computational expense. We present a randomized wrapper algorithm whose computational requirements are within a constant factor of simply learning in the presence of all input variables, provided that the number of relevant variables is small and known in advance. We then show how to remove the latter assumption, and demonstrate performance on several problems.
暂无评论