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This paper proposes a hybrid method combining support vector regression (SVR) and fuzzy inference method for one-day ahead hourly forecasting of photovoltaic (PV) power output. The proposed method comprises training stage and forecasting stage. In the training stage, a number of SVR models are used to learn the collected input/output data sets. To achieve accurate forecast, the fuzzy inference method...
In this paper, an averaged model of circulating current is proposed. Based on the model, the mechanism concerned with the circulating current occurring are explained explicitly. Owing to the circulating current which mainly consists of zero-sequence components varies with different factors such as pulse width-modulation strategies, circuit parameters and so on, therefore a new control variable associated...
The improved algorithm of WNN based on BP was proposed in this paper. Theoretical analysis and simulation result show it avoids both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations, such as local optimization. So it can simplify the training of neural networks. It has better abilities in function learning and generalization. This algorithm was successfully...
This paper has presented an effective and efficient approach to extract diagnosis rules from inconsistent and redundant data set of power transformers using rough set theory. The extracted diagnosis rules can effectively reduce space of input attributes and simplify knowledge representation for fault diagnosis. The fault diagnosis decision table is first built through discretized attributes. Next,...
A k-nearest neighbor classifier with online learning procedure for steady-state security assessment is introduced. A dynamic sample set and the related sample editing strategies are proposed. The dynamic samples can keep tracking the operation status of power system to minimize classification error. It is implemented through editing the dynamic samples according to their online performances. The classification...
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
Neural networks (NNs) have been widely used to financial risk management because of their excellent performances of treating non-linear data with self-learning capability. However, the shortcoming of neural networks is also significant due to a "black box" syndrome and the difficulty in dealing with qualitative information, which limited its applications in practice. To overcome these drawbacks...
We use the multilayer perceptron for well log data inversion. The gradient descent method is used in the back propagation learning rule. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original and the higher order features are used for the training process. According to our experimental results, the expanding higher...
Supply chain management includes four major elements; namely, manufacturers, suppliers, distributors and retailers. Inventory control plays a very important role in each of the four modules in the supply chain. In this paper, a decision surface modeling tool is developed using neural networks. It is capable of capturing the essential features of the retail simulation model in multidimensional, mathematical...
A hierarchical system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures are type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Also we use the minimum error to determine...
Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental...
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