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We investigate the global throughput maximization of distributed spectrum reusing (DSR) in cognitive radio (CR) network, which reaches the two-dimensional spectrum multiplexing. Most previous works only consider the temporal-domain accessing, which greatly underutilize the spectrum resources. In this paper, we propose a new temporal-spatial spectrum reusing scheme by fully exploiting the location...
Due to the rising signal speed in today's integrated circuits (ICs), the digital input/output (I/O) device modeling becomes a very serious challenge. However, its nonlinearity issue was even less addressed. But for accurate EMC and EMI characterizations, the I/O nonlinearity could become a source of unexpected EMC and EMI troubles in the high-speed system. In this paper, we analyze the nonlinearity...
Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned...
Edge detection is an important but rather difficult task in image processing and analysis. In this research, artificial neural networks are employed for edge detection based on its adaptive learning and nonlinear mapping properties. Fuzzy sets are introduced during the training phase to improve the generalization ability of neural networks. The application of the proposed neural network approach to...
Bridge safety evaluation is very important to ensure the safe work of bridge. In the study, RBF neural network with genetic algorithm is presented to evaluate bridge safety, genetic algorithm is adopted to select the parameters of RBF neural network to improve the performance of RBF neural network. The influencing factors and evaluation index of bridge safety evaluation are determined. Then, the experimental...
Least squares support vector machines (LSSVM) has been carried out in order to obtain a statistically meaningful analysis of the extended set of molecules. The combined HF with LSSVM correction approach (LSSVM/HF) has been applied to evaluate the transition energies of organic molecules. After LSSVM correction, the RMS deviations of the calculated transition energies reduce from 0.91 to 0.26 eV for...
For the re-evolution of the mobile robot behavior in unknown environments, the mapping relation was constructed between input of sensors and output of actuators based on echo state network. An algorithm of adaptive behavior learning was presented based on echo state network for evolutionary robotics. The composite architecture with responsive behavior and behavior learning was adopted. The responsive...
Owing to the original Ott-Grebogi-Yorke(OGY) method can only be applied to the discrete dynamical systems or continuous dynamical systems which can be described by Poincare mapping, a novel method for controlling chaos is proposed by resorting to echo state network. The network was trained by the input and output sample which were generated by OGY method. The chaos controller can drive the chaotic...
The complex Interconnections between markers and polygenic genotype value suggested that the regression was not enough for describing the relation between genes and traits. Artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems. Recently, artificial neural networks had been successfully used to predict the polygenic genotype value, and the different learning...
Least squares support vector machines (LS-SVM) was introduced to improve the calculation accuracy of low level density functional theory. As a demonstration, this combined low level quantum mechanical calculation with LS-SVM correction approach has been applied to evaluate the absorption energies of 160 organic molecules. After LS-SVM correction, the RMS deviations of the calculated absorption energies...
In the past, a prediction equation based on the single nucleotide polymorphisms (SNP) is derived to calculate genomic breeding values (GEBV). However, the genome is very complex; a function could not reflect the relation between markers and phenotypes. Unlike the methods of regression, artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems, however, artificial...
Although linear multivariate approaches used to analyze large genetic data sets did not allow a large part of the total variance to be explained, strong distortions with nonlinear data sets, horseshoe effects had always been found. Artificial neural networks could gather their knowledge by detecting the patterns and relationships in data and learn through experience, and could perform well for optimization...
In this paper, we propose a family of space-time block codes (STBC) that achieve full diversity when linear receivers, such as zero-forcing (ZF) or minimum mean square error (MMSE) receivers, are used. Our proposed STBC family is a combination/overlay between orthogonal STBC and Toeplitz codes, which can be viewed as a generalization of overlapped Alamouti codes (OAC) and Toeplitz codes recently proposed...
Incremental learning is attracting more and more interest in the field of machine learning due to its wide potential applications in many scientific and engineering areas. Negative correlation learning (NCL) (Liu and Yao; 1999a,b) is a successful approach to construct neural network ensembles. By encouraging the diversity of ensembles, it makes different neural networks to learn different knowledge...
A roboticized rock abrasion tool is considered to replace the geologistpsilas rock hammer to remove dusty and weathered surfaces of the rock. A roboticicized rock abrasion tool with three degrees of freedom has been developed in this paper. Planetary transmission system is used in the grinding driving system with two inputs (rotation motor and revolution motor) and two outputs (grinding wheel and...
An improved particle swarm optimizer based on differential evolution theory is proposed. This algorithm introduces differential mutation operator into the basic particle swarm optimizer in order to solve the premature convergence problem. And this new algorithm was used to training weights and thresholds of feedforward neural network, simulation results show that this approach is effective and has...
Induction motors have some inherent characteristics such as multivariate, parameter indeterminacy, strong coupling and non-linearity. These bring about a lot of trouble to the induction motor drive system. Considering the problems of AC speed regulation cause by the motor's inherent characteristics, a fuzzy control strategy based on the RBF neural network was presented. It was designed to make full...
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule...
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