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In this paper, based on investigating and analyzing on the affecting factors for support type of development roadways as well as the successful support cases in Chengchao Iron mine, the improved BP neural network is put forward to study on the support type of development roadways. It may be seen from the learning course of learning samples and the prediction results of support types that whether the...
In linear space, the classical perceptron algorithm is simple and practical. But when concerning the nonlinear space it is severely confined mainly on its signal layer structure. This paper analyzes the geometry characteristic of solve region in the pattern set, and presents a new algorithm based on the solve region. The new algorithm could find the better solve vector in the solve region on condition...
SOM neural network is one of the most commonly used Clustering algorithm in the text clustering. The initial connection weights of SOM neural network will affect the degree of convergence. If the Initial connection weights are not set appropriate, that will cause in a long wandering around the local minimum, accordingly lower the speed of convergence, or even cause local convergence or not convergence...
Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible...
Three term backpropagation (BP) network as proposed by Zweiri in 2003 has outperformed standard two term backpropagation. However, further studies on three term backpropagation in 2007 indicated that this network only surpassed standard BP for small scale datasets but not for medium and large scale datasets. It has also been observed that by using mean square error (MSE) as a cost function in three...
This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods...
We propose a novel non-parametric adaptive outlier detection algorithm, called LPE, for high dimensional data based on score functions derived from nearest neighbor graphs on n-point nominal data. Outliers are predicted whenever the score of a test sample falls below ??, which is supposed to be the desired false alarm level. The resulting outlier detector is shown to be asymptotically optimal in that...
Because there were a lot of facts that affect the intensity of coal and gas outburst, a BP neural network model for forecasting the intensity was constructed. Aimed at the shortcoming of the BP neural network, such as the slow training speed, easy to be trapped into the local optimums, and the premature convergence of genetic algorithm (GA) BP neural network, a method to design the BP neural network...
The commercial banks risks come from all the uncertainty of the banking business, which have diffusibility and hidden features, if not timely controlled, will have a negative impact on the national economy. Therefore, it is necessary to design the corresponding index system according to the objectivity and relativity of the banking risks, and then control quantitatively the banks risk. Based on the...
This paper studies various training algorithms of BP neural network and proposes an improved conjugate gradient algorithm which combines conjugate gradient algorithm with inexact line search route based on generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied...
A new approach for wide-range optimal reactor temperature control using diagonal recurrent neural networks (DRNN) with an adaptive learning rate scheme is presented. The drawback of the usual feedforward neural network (FNN) is that it is a static mapping and requires a large number of neurons and takes a long training time. The usual fixed learning rate based on an empirical trial and error scheme...
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