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An Efficient Predistorter Design for Compensating Nonlinear Memory High Power Amplifiers
"... Abstract—This contribution applies digital predistorter to compensate distortions caused by memory high power amplifiers (HPAs) which exhibit true output saturation characteristics. Particle swarm optimization is first implemented to identify the Wiener HPA’s parameters. The estimated Wiener HPA mod ..."
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Abstract—This contribution applies digital predistorter to compensate distortions caused by memory high power amplifiers (HPAs) which exhibit true output saturation characteristics. Particle swarm optimization is first implemented to identify the Wiener HPA’s parameters. The estimated Wiener HPA model is then directly used to design the predistorter. The proposed digital predistorter solution is attractive owing to its low online computational complexity, small memory units required and simple VLSI hardware structure implementation. Moreover, the designed predistorter is capable of successfully compensating serious nonlinear distortions and memory effects caused by the memory HPA operating in the output saturation region. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design. Index Terms—Hammerstein model, memory high power amplifier, output saturation, particle swarm optimization, predistorter, Wiener model. I.
Elasticnet prefiltering for twoclass classification
 IEEE Trans. Cybern. 43 (February
, 2013
"... Abstract—A twostage linearintheparameter model construction algorithm is proposed aimed at noisy twoclass classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linearinthep ..."
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Abstract—A twostage linearintheparameter model construction algorithm is proposed aimed at noisy twoclass classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linearintheparameter classifier. The prefiltering stage is a twolevel process aimed at maximizing a model’s generalization capability, in which a new elasticnet model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particleswarmoptimization algorithm at the upper level by minimizing the leaveoneout (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the Doptimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems. Index Terms—Crossvalidation (CV), elastic net (EN), forward regression, leaveoneout (LOO) errors, linearintheparameter model, regularization. I.
ROBOT SENSOR CALIBRATION VIA NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION ENHANCED WITH CROSSOVER AND MUTATION
"... Original scientific paper In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of handeye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle ..."
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Original scientific paper In order to determine the position and orientation of an object in the wrist frame for robot, transform relation of handeye system should be estimated, which is described as rotational matrix and translational vector. A new approach integrating neural network and particle swarm optimization algorithm with crossover and mutation operation for robot sense calibration is proposed. First the neural network with rotational weight matrix is structured, where the weights are the elements of rotational part of homogeneous transform of the handeye system. Then the particle swarm optimization algorithm is integrated into the solving program, where the inertia weight factor and mutation probability are tuned selfadaptively according to the motion trajectory of particles in longitudinal direction and lateral direction. When the termination criterion is satisfied, the rotational matrix is obtained from the neural network’s stable weights. Then the translational vector is solved, so the position and orientation of camera frame with respect to wrist frame is achieved. The proposed approach provides a new scheme for robot sense calibration with selfadaptive technique, which guarantees the orthogonality of solved
Neurocomputing 122 (2013) 210–220 Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/neucom Particle swarm optimisation assisted classification ..."
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journal homepage: www.elsevier.com/locate/neucom Particle swarm optimisation assisted classification
Readouts for EchoState Networks Built using Locally Regularized Orthogonal Forward Regression
"... Echo state network (ESN) is viewed as a temporal nonorthogonal expansion with pseudorandom parameters. Such expansions naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echoregressors effectively explain the ..."
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Echo state network (ESN) is viewed as a temporal nonorthogonal expansion with pseudorandom parameters. Such expansions naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echoregressors effectively explain the variance of the teacher output and also that sole local regularization is not able to provide indepth information concerning the importance of the generated regressors. The importance is therefore determined by a joint calculation of the individual variance contributions and Bayesian relevance using locally regularized orthogonal forward regression (LROFR) algorithm. This information can be advantageously used in a variety of ways for an indepth analysis of an ESN structure and its statespace parameters in relation to the unknown dynamics of the underlying problem. We present locally regularized linear readout built using LROFR. The readout may have a different dimensionality than an ESN model itself, and besides improving robustness and accuracy of an ESN it relates the echoregressors to different features of the training data and may determine what type of an additional readout is suitable for a task at hand. Moreover, as flexibility of the linear readout has limitations and might sometimes be insufficient for certain tasks, we also present a radial basis function (RBF) readout built using LROFR. It is a flexible and parsimonious readout with excellent generalization abilities and is a viable alternative to readouts based on a feedforward neural network (FFNN) or an RBF net built using relevance vector machine (RVM).
On Combination of SMOTE and Particle Swarm Optimization Based Radial Basis Function Classifier for Imbalanced Problems
"... Abstract—The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced twoclass data. In order to enhance the significance of the small and specific region belonging to the positive cla ..."
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Abstract—The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced twoclass data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the oversampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leaveoneout misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm. I.
Synthetic minority oversampling
"... s a ino is f ging es f RB assi PSO algorithm based on the criterion of minimising the leaveoneout misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorith ..."
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s a ino is f ging es f RB assi PSO algorithm based on the criterion of minimising the leaveoneout misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm. d to a nown er clas data re ntrast