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Choquet integral with regards to a non-additive set function μ is a useful combination tool when we consider the interactions between classifiers. This combination method works very well at the expense of run time and the memory space. This paper introduces samples reduction technology to degrade the complexity of determining the non-additive set functions μ which is determined by genetic algorithm...
Supervised classification of fully polarimetric SAR image using neural network is a common method nowadays. As an effective learning method of neural network, BP algorithm is the most widespread one in the neural network algorithms. However, BP network is easy to fall into local extremum and exists shortcomings such as the slow training process. To this end, this paper presents a method of supervised...
As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can...
In this paper, a new prediction model, based on chaos theory and BP artificial neural network, is developed to predict the risk of credit card transactions. Embedding dimension of phase-space reconstruction is used to determine network structure, and overcomes the dependence on large amount of samples. Experiments shows that the method based on combination of chaos theory and neural network can improve...
A new dynamic selective neural network ensemble method for fault diagnosis of steam turbine is proposed. Firstly, a great number of diverse BP neural network models are produced. Secondly, the error matrix is calculated and the K-nearest neighbor algorithm is used to predict the generalization errors of different neural networks on each testing sample. Thirdly, the individual networks whose generalization...
In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learn assignment, slow convergence, and local minimal in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, it has been proved that the model is time-consuming...
After analyzing the bidding tactics decision-making index system for BOT projects, the bidding tactics decision-making method is presented by wavelet network learning algorithm based on conjugate gradient method, the decision-making model is set up. The paper bring forward the problem solving method to the above model by optimizing and select the hidden units of wavelet network. An instance is given...
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