2,998 research outputs found
Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems
A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the systemâs channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark
Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems
In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called âoverloadedâ multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements. Index TermsâClassification, multiple-antenna system, orthogonal forward selection, radial basis function (RBF), symmetry
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
Teleoperation control based on combination of wave variable and neural networks
In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov-based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods
Adaptive NN output-feedback control for stochastic time-delay nonlinear systems with unknown control coefficients and perturbations
This paper addresses the problem of adaptive output-feedback control for more general class of stochastic time-varying delay nonlinear systems with unknown control coefficients and perturbations. By using LyapunovâKrasovskii functional, backstepping and tuning function technique, a novel adaptive neural network (NN) output-feedback controller is constructed with fewer learning parameters. The designed controller guarantees that all the signals in the closed-loop system are 4-moment (or mean square) semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme
Research on parallel nonlinear control system of PD and RBF neural network based on U model
The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system
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