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Face is a complex multidimensional visual model and it is difficult to develop a computational model for recognition. A novel approach is presented to face recognition in this paper, which uses wavelet transform (WT), fast independent component analysis (FastICA) and radial basis function (RBF) neural networks. Firstly, low frequency subband images are extracted from original face image by 2D wavelet...
In this paper, fault diagnosis model based on the fusion of hybrid neural network (HNN) and ant colony optimization algorithm (ACOA) is presented. The associated rules are extracted based on rough set theory and are used as the theoretic basis of the connection mechanism of higher-order NN, which was composed with the feedforward NN, the hybrid NN model is constructed. ACOA was used to optimize solving...
In this paper, a novel approach based on BP network (BPN) for fault diagnosis of power transformers is proposed. Optimization of BPN weights is achieved by using clonal selection algorithms (CSA). In addition, the mutation probability is adjusted adaptively according to the affinity of antibody. Compared with previous approaches, this one can avoid prematurity effectively, with good self-learning...
In this work we consider the Bayesian Integrate And Shift (BIAS) model for learning object categories and investigate its sensitivity to changes in the sizes and locations of fixation regions. We test the model using a face category and show that the learning algorithm is robust to large variations of the regions' sizes and locations. Specifically, we show that the performance is inversely proportional...
A back-propagation (BP) neural networks model was used for simulating daily streamflows in the upper area of Nangao Reservoir at Shanwei City, Guangdong Province, China. Approaches and techniques of applying the BP model in runoff simulation are presented in this paper. A comparison of the BP model to the Xinanjiang model was also conducted to evaluate the performance of the BP model. The simulated...
An attempt has been made to model a production engine control module. Both extended Kalman filter (EKF) algorithm and iterated extended Kalman filter (IEKF) algorithm are used in the construction of the model. The results shows the model trained by both algorithms can produce accurate results with RMS errors in a range of 2- 3%, while iterated extended Kalman filter algorithm outperforms the extended...
The oilfield remaining oil distribution forecast is called world-level difficult problems by oil domain specialists in the world. The source of low forecast correctness are only consider objective evidences or subjective evidence, so the forecast results still exist limitation, it result in low accuracy, reliability and so on to identify the classification characteristics and to compute quantitative...
The mine water system is a nonlinear and dynamic system influenced by such factors as hydrological and geological conditions. Although neural network model can effectively solve the nonlinear problems, it's difficult to obtain the satisfying results by direct prediction for the time series of water yield of mine because of chaotic characteristics in the system. In order to discover the nonlinear structure...
As conventional multilayer backward-propagation network does not perform well on parameter estimation and convergence, several improved backward-propagation algorithms, such as VLBP, MOBP, CGBP and LMBP, were developed. In order to investigate simulation performance of each algorithm to construct the BP network model suitable for hydrological forecasting, five backward-propagation (BP) neural networks...
An accurate prediction of gas emission volume under the shaft is the premise for the prevention of gas explosion. To more accurately predict the gas emission volume, a novel wavelet neural network is proposed in this paper. First, a new structure of wavelet neural network is established. This structure is a kind of compact metric structure, in which the Daubechies wavelet is adopted. And then genetic...
The BP neural network characteristic has been summarily analyzed. Based on its error back propagation method, the peculiarity of modifying its weightiness and threshold value to make the calculated error come down along the negative gradient direction, the article proposed a new approach that used the weightiness distribution between the input and output layer to appraise the input parameters' significant...
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