Results 211 to 220 of about 59,339 (255)
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A RBF neural networks based feature
2010 8th World Congress on Intelligent Control and Automation, 2010A RBF (Back-Propogation) neural networks based feature is applied to the target recognition, which aims at only recognition of the target feature and searches the hyperplane of the local space taking the target feature as center. The classifier integrates the target feature with RBF ANNs.
Lianglong Da +3 more
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Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters, 2009Multi-label learning deals with the problem where each instance is associated with multiple labels simultaneously. The task of this learning paradigm is to predict the label set for each unseen instance, through analyzing training instances with known label sets.
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L p approximation capability of RBF neural networks
Acta Mathematica Sinica, English Series, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nan, Dong +4 more
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Adaptive RBF Neural Network Control
2012Since the idea of the computational abilities of networks composed of simple models of neurons was introduced in the 1940s [1], neural network techniques have undergone great developments and have been successfully applied in many fields such as learning, pattern recognition, signal processing, modeling, and system control.
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Harmonic identification based on RBF neural network
2016 35th Chinese Control Conference (CCC), 2016As one of the important equipment for vibration test, hydraulic shaking table can produce great vibration force and displacement of vibration, , widely applied in engineering field. In the test system of hydraulic shaking table, because of the existence of non-linearities, there exists higher harmonic in system response signal when the shaking table is
Jianjun Yao +5 more
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Corrective action planning using RBF neural network
Applied Soft Computing, 2007In recent years, voltage limit violation and power system load-generation imbalance, i.e., line loading limit violation have been responsible for several incidents of major network collapses leading to partial or even complete blackouts. Alleviation of line overloads is the suitable corrective action in this regard.
Daya Ram +3 more
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Digital RBF Neural Network Control
2012This chapter introduces adaptive Runge–Kutta–Merson method for digital RBF neural network controller design. Two examples for mechanical controls are given, including digital adaptive control for a servo system and digital adaptive control for two-link manipulators.
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Accuracy versus complexity in RBF neural networks
IEEE Instrumentation & Measurement Magazine, 2001We have introduced a methodology for solving the tradeoff between accuracy and complexity in complex virtual systems directly at the system level. Such methodology can be inserted in an application-level compiler for transforming a high-level description of the application into a lower level.
C. Alippi, V. Piuri, F. Scotti
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Discrete RBF Neural Network Control
2017The discrete-time implementation of controllers is important. There are two methods for designing the digital controller. One method, called emulation, is to design a controller based on the continuous-time system, then discrete the controller.
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Fuzzy Calculus by RBF Neural Networks
2003The paper presents novel modeling of fuzzy inference system by using the ‘fuzzified’ radial basis function (RBF) neural network (NN). RBF NN performs the mapping of the antecedent fuzzy numbers (a.k.a. membership functions, attributes, possibilities degrees) into the consequent ones. In this way, an RBF NN is capable of performing the rigorous calculus
Vojislav Kecman, Zhenquan Li
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