An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The st...
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An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.
This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and bp （back propagation） algorithm. Here, DE and bp are combi...
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This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and bp （back propagation） algorithm. Here, DE and bp are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.
This paper presents a half-autonomous navigation method of the Lunar Vehicle, then discuss the implementation of the case-based learning method by using Neural Network. At the end, we discuss the improvement of bp alg...
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ISBN:
(纸本)7312012035
This paper presents a half-autonomous navigation method of the Lunar Vehicle, then discuss the implementation of the case-based learning method by using Neural Network. At the end, we discuss the improvement of bp algorithm using Adaptive Learning method.
A new synthetic method of neural network and mechanistic model is presented in this paper. This method merges the mechanistic model knowledge with the neural network structure. It makes the number of weights of networ...
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ISBN:
(纸本)7312012035
A new synthetic method of neural network and mechanistic model is presented in this paper. This method merges the mechanistic model knowledge with the neural network structure. It makes the number of weights of network greatly reduce. The learning speed, nonlinear approximation precision and generalization performance of the network are improved. The effect of application in rolling force prediction is very excellent.
A new improved genetic bp algorithm was put forward in the paper. To determine whether the network falls into local minimum point, a discriminant of local minimum was put forth in the training process of a neural netw...
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A new improved genetic bp algorithm was put forward in the paper. To determine whether the network falls into local minimum point, a discriminant of local minimum was put forth in the training process of a neural network. A genetic algorithm was used to revise the weights of the neural network if the bp algorithm fell into minimums. The mechanical faults were diagnosed using the algorithm put forward in the paper, which verified the validity of this improved genetic bp algorithm.
Based on fuzzy mathematics and the neural network method, with the combination of fault datas of B737NG aircraft flight control system and experts experiences, this paper built a fuzzy neural network (FNN) fault diagn...
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Based on fuzzy mathematics and the neural network method, with the combination of fault datas of B737NG aircraft flight control system and experts experiences, this paper built a fuzzy neural network (FNN) fault diagnosis model which can intuitively express the relevance of fault symptoms and fault reasons. By Simulation and analysis, the fault diagnosis accuracy of FNN is much higher than only bp neural network, The research makes fault positioning more accurate and valid, improves the efficiency of maintenance, as well as ensures flight safety and provides a new point of view for establishing the aviation maintenance auxiliary decision system.
This paper proposes a short-term load forecasting method based on combination of ANN and fuzzy control. Improved bp algorithm is obtained by optimizing training samples, varying step and factor. In order to eliminate ...
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This paper proposes a short-term load forecasting method based on combination of ANN and fuzzy control. Improved bp algorithm is obtained by optimizing training samples, varying step and factor. In order to eliminate forecast error, on-line self-tuning fuzzy control is used. Simulation results demonstrate that the proposed method improves the forecast precision.
This paper presents a fast Neural Network algorithm, in which the step is regarded as the function of the error and the output function of network node, and weight is calculated by different step. By adopting the fast...
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This paper presents a fast Neural Network algorithm, in which the step is regarded as the function of the error and the output function of network node, and weight is calculated by different step. By adopting the fast NN algorithm, we developed a speaker-independent speech recognition system. The experiment shows that the new algorithm is over 10 times faster than the traditional bp algorithm and has better performance and spreading ability.
Vibrating mills play an important role in the field of preparation of ultrafine powder. The purpose of a vibrating mill load control system is to increase productivity and ensure the mill runs smoothly. In this paper,...
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Vibrating mills play an important role in the field of preparation of ultrafine powder. The purpose of a vibrating mill load control system is to increase productivity and ensure the mill runs smoothly. In this paper, we first summarized prior knowledge of control rules on the basis of analysis after repeated experiments, and realized fuzzy automation taking advantage of fuzzy theory. Since fuzzy control systems not only over rely on experience but also lack a self-learning function, we design a fuzzy neural network control system （FNNC） in order to improve the control system self-learning function and adaptive capacity while working conditions change. We adjust and optimize network *** using a back propagation（bp） algorithm. Simulation results show the control system dynamic performance is significantly improved, overshoot reduced from 23 % to 8 % and rise time shortened 0. 4 min.
In this paper, the bp neural network method for segmenting the target and detecting the edge from the color image was studied. The samples and their corresponding binary images for bp neural network training were deri...
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In this paper, the bp neural network method for segmenting the target and detecting the edge from the color image was studied. The samples and their corresponding binary images for bp neural network training were derived from the ultrasonic CT images in concrete's non-destructive detecting. The experimental results showed that the segmentation and the edge detecting effects of the bp network method were satisfying. Easy to design was the best advantage of such a method. However the drawback was that bp neural network need too much time for learning.
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