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Aiming at the problem of network security situation prediction, this paper studies the prediction method based on RBF neural network. Through training the RBF neural network, find out the nonlinear mapping relationship between the front N data and the subsequent M data, and then adjust the value of N to explore the different prediction results. The simulation result shows that the proposed method...
This paper investigates the wind speed forecast for the stratospheric airship fixed over a geo-location because the wind speed forecast is a key challenge for the airship station-keeping control. In view of the wind speed series which changes with the time and space and shows the non-linear and non-stationary characteristics, this paper put forward a kind of adaptive model based on Incremental extreme...
In this paper, an algorithm that based on pca-bp-bagging model is developed for the prediction of pathological data. This algorithm aims at improving the characteristics of bp neural network that the prediction accuracy of pathological data is low, the generalization ability of single bp neural network model is poor, and the anti-interference ability is weak. To enhance the performance of the whole...
To solve the problem of gradient descent (GD) method which has low accuracy and easily falling into local optimum, the radial basis function (RBF) based on immune algorithm system (IAS-RBF) is proposed. In this method, each antibody is a RBF neural network and the optimal affinity is calculated by immune algorithm system (IAS) to get the best antibody, then the optimal parameter of RBF neural network...
Neural network is a kind of machine learning algorithm, applied in many ways. The traditional predictive guidance of aerocraft is hard to resolve the contradiction among robustness, real-time and the guidance of precision. The paper provides a predictive guidance algorithm for aerocraft, by combining neural network with predictive guidance to solve this problem. This research about the new style guidance...
Twin support vector regression and its extensions have been widely applied in machine learning and data mining. However, most of them can not achieve the satisfactory performances when the noise is involved. To this end, this paper presents a weighted least squares twin support vector regression (WLSTSVR) which can reduce the influence of the noise on prediction accuracy by using the information of...
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