Application Research on Improved Fusion Algorithm Based on BP Neural Network and POS

Abstract

Optimization problem is the problem which can be often encountered mostly in industrial design, and the key of optimization is to find the global optimum and higher constriction speed. This paper proposes a PSO algorithm based on BP neural network by neural network trains and selects individual extreme best randomly, to make the particle follow the optimal particle in the solution space search, and obtain the optimum extreme best in the whole situation. Through the application of the simulation experiment on image segmentation showed that the algorithm is suitable in dealing with multiple types function and constraint, with fast convergence speed, and easy combination with traditional optimization methods, thus improving its own limitations, and solving problems more efficiently.

The Promotion Path of Pseudo and Real Human Settlements Environment Coupling Coordination in Resource-Based Cities

“…Others used the analytic hierarchy process to evaluate the living environment of Shanxi Province. Some scholars have combined the BP neural network and genetic algorithm to construct an urban living environment quality evaluation system, reflecting the nonlinear characteristics of the system [11,12].…”

An Integrated Artificial Neural Network-based Precipitation Revision Model

“…Therefore, the reasonable number of hidden layer nodes should be determined by considering the network structure complexity and the error size. Common methods include the node deletion method and extension method [24]. According to the node deletion and expansion methods, three types of node numbers (15, 25, and 35) are used to compare the effects of different numbers of hidden layer nodes on precipitation forecast data revisions.…”

Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network

“…Therefore, to the image feature extraction process, the Gabor image feature extraction is to conduct the convolution of input images and the Gabor wavelet described in Equation (4). It is assumed that the input image grey scale is I(x, y) and the convolution between I and the Gabor core, G u,v , is shown in Equation (5) …”

“…Therefore, to the image feature extraction process, the Gabor image feature extraction is to conduct the convolution of input images and the Gabor wavelet described in Equation (4). It is assumed that the input image grey scale is ( , ) I x y and the convolution between I and the Gabor core, Wavelet transform has the following advantages [17] when being applied to image processing: (1) wavelet decomposition can cover the whole frequency domain; (2) by choosing the proper filter, the wavelet filter can largely reduce or even remove the relevance between different characteristics extracted; (3) the wavelet transform has a "zooming" characteristic and can adopt the wide analysis window in the low-frequency section and the narrow analysis window in the high-frequency section.…”

“…Currently, scholars have deepened their study of neural network theories. The artificial neural network [4,5] is an intelligent system simulated by humans according to information processing of the human brain nerve system, and a new-type structural computing system generated based on a preliminary understanding of the human brain organization structure and activity mechanism. Since it can simulate the human brain nerve system and endow the machine with the perception, learning, and deduction capability of the human brain, it has been widely applied to the model recognition in various fields.…”