To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accur...

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to...

Neural decoding is an important task for understanding how the biological nervous system performs computation and communication. This paper introduces a novel continuous neural decoding method based on general regression neural network (GRNN). GRNN does not require an iterative training procedure as in other neural networks and allows the appropriate regression form to be expressed as a probabi...

The General Regression Neural Network (GRNN) is briefly introduced. The BIC method for determining the order of Auto Regression (AR) model is employed to select the number of input neurons, and the Genetic Algorithm is applied to calculate the optimal smoothing parameter. The GRNN is used to predict the vibration time series of a large turbo-compressor, and its performance is compared with that...

The process of bid/no-bid decision-making is su bjected to uncertainty and influence of complex criteria. This paper proposed an application of the integration of rough sets (RS) and improved general regression neural network (GRNN) based on niche particle swarm optimization (NPSO) algorithm for tendering decision making. The decision table of RS and the attribution reduction was processed by M...

With the development of computer technology, there are more and algorithms models for data processing analysis, which brings a new direction to radar target recognition. This study mainly analyzed recognition high resolution range profile (HRRP) in applied generalized regression neural network (GRNN) model HRRP In order improve performance HRRP, fruit fly optimization algorithm (FOA) was improv...

Recently, neural network based dependency parsing has attracted much interest, which can effectively alleviate the problems of data sparsity and feature engineering by using the dense features. However, it is still a challenge problem to sufficiently model the complicated syntactic and semantic compositions of the dense features in neural network based methods. In this paper, we propose two het...

0950-7051/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.knosys.2012.08.015 ⇑ Corresponding author. Tel.: +86 15811424568; fa E-mail address: [email protected] (S. Guo). Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power indus...

Accurate and stable prediction of icing thickness on transmission lines is of great significance for ensuring the safe operation of the power grid. In order to improve the accuracy and stability of icing prediction, an innovative prediction model based on the generalized regression neural network (GRNN) and the fruit fly optimization algorithm (FOA) is proposed. Firstly, a feature selection met...

Accurate and reliable forecasts of diarrhea incidences are necessary for the health authorities to ensure the appropriate action for the control of the outbreak. In this paper, a novel hybrid model known as EEMD-GRNN is proposed to forecast the diarrhea incidences. The proposed approach first uses Ensemble Empirical Mode Decomposition (EEMD), which can adaptively decompose the complicated raw t...