Aiming at the problems of network flow monitoring (classification) in complex network environment, a stochastic artificial neural network learning method was proposed to realize the direct classification of multiple classes and improve the training speed of learning methods. Using Plane-Gaussian (PG) artificial neural network model, the idea of stochastic projection was introduced, and the network connection matrix was obtained by calculating the pseudo-inverse analysis. Theoretically, it can be proved that the network has global approximation ability. The artificial simulation was carried out on artificial data and standard network flow monitoring data. Compared with the Extreme Learning Machine (ELM) and PG network using the random method, the analysis and experimental results show that: 1)the proposed method inherits the geometric characteristics of the PG network and is more effective for the planar distributed data; 2)it has comparable training speed to ELM, but significantly faster than PG network; 3)among the three methods, the proposed method is more suitable for solving the problem of network flow monitoring.

Based on smooth spline principle, a self-adaptive multi-threshold segmentation algorithm HistSplineReg (Spline Regression for Histogram) was proposed. HistSplineReg is a two-step method. Firstly, a smoothing spline function was regressed to fit the one-dimensional image histogram, and then the extreme value was found by the regression function to achieve multi-threshold automatic segmentation of the image. Compared to the existing multi-threshold methods, the advantages of HistSplineReg lie in 5 aspects:1) it is quite consistent with the human intuition; 2) it is constructed on the smoothing spline, which is a solid mathematic basis; 3) both the number and the size of multiple thresholds can be automatically determined; 4) HistSplineReg can be analytically solved, and its computing burden is mainly concentrated on Cholesky decomposition of the matrix, while the size of matrix depends on the pixel level of the image, rather than the scale of the image; 5) it has only one trade-off parameter to balance the empirical error and regressor's smoothness. Furthermore, for the forest fire recognition task, an experimental reference value was provided. Finally, experiments were conducted on some digital forest fire images in the RGB (Red, Green, Blue) mode. The experimental results show that the histSplineReg method is more effective than Support Vector Regression (SVR) and Polynomial Fitting (PolyFit), which is based on the grayscale image, the color channel, the color image synthesized by each channel segmentation. And the three methods all reflect the red channel information is most significant to the forest fire image segmentation effect.