In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects. 相似文献
Over the past decade, object recognition work has confounded voxel response detection with potential voxel class identification. Consequently, the claim that there are areas of the brain that are necessary and sufficient for object identification cannot be resolved with existing associative methods (e.g., the general linear model) that are dominant in brain imaging methods. In order to explore this controversy we trained full brain (40,000 voxels) single TR (repetition time) classifiers on data from 10 subjects in two different recognition tasks on the most controversial classes of stimuli (house and face) and show 97.4% median out-of-sample (unseen TRs) generalization. This performance allowed us to reliably and uniquely assay the classifier's voxel diagnosticity in all individual subjects' brains. In this two-class case, there may be specific areas diagnostic for house stimuli (e.g., LO) or for face stimuli (e.g., STS); however, in contrast to the detection results common in this literature, neither the fusiform face area nor parahippocampal place area is shown to be uniquely diagnostic for faces or places, respectively. 相似文献
Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability
and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds
of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives
in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership
functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary
algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that
could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work
presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still
accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two
different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search
ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the
same time simpler models with respect to the single objective based approach.
A programming language that considers basic values and classes as objects brings more opportunities of code reuse and it is easier to use than a language that does not support this feature. However, popular statically typed object-oriented languages do not consider classes as first-class objects because this concept is difficult to integrate with static type checking. They also do not consider basic values as objects for sake of efficiency. This article presents the Green language type system which supports classes as classless objects and offers a mechanism to treat basic values as objects. The result is a reasonably simple type system which is statically typed and easy to implement. It simplifies several other language mechanisms and prevents any infinite regression of metaclasses. 相似文献
Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non-expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process. 相似文献
This paper aims to contribute to the goal of finding influential legal precedents by quantitative methods. A lot of work has been made in this direction worldwide, especially in the context of common law jurisdictions. However, this type of work is extremely scarce in the Brazilian literature. In addition, our work also contributes to the research of network analysis and the law by applying these methods to unprecedented amount of data and narrowing our inquiry to a single law area, corporate law. Furthermore, whereas most of the literature applying network analysis to judicial decisions had access to readily available data on the citations to precedent within each ruling, our raw data was nothing but the full text of decisions. We focus on data produced by the Superior Court of Justice (STJ), the highest court in Brazil for matters of federal law, including statutory interpretation of civil, criminal and corporate law. The Court issued an astonishing 282040 opinions tagged as related to corporate law between 2008 and 2018. This amount of cases is unparalleled internationally for superior courts and for studies in network analysis and law. In our results, we rank precedents quantitatively based on the citations they receive and make. We also qualitatively analyze some of the results, especially related to groups identified in the network with the Modularity algorithm. Our findings also reveal that corporate law jurisprudence in the STJ is quantitatively dominated by a few legal issues around one single theme that is only tangentially related to corporate law. That is, a type of contract used for the expansion of telephone landlines, which also allowed the consumer to become a shareholder of the telecommunication company. This comparison is especially pertinent because the utter lack of data on the quantitative weight of STJ precedents means the national literature has been operating in a void of objective measurements, one which has been filled with cherry-picked rulings and subjective ranking criteria.