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With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
The problem of environmental quality assessment is a pattern recognition problem, and a well-trained ANN can exploit the underlying nonlinear relationships that determine the environmental rating of a region. In this study, we are trying with the neural network model to make an effective analysis for environmental quality assessment. A 4-9-1 three-layer feedforward neural network using the backpropagation...
Protein identification using mass spectrometry is a critical step in many areas of the life sciences, and in proteomics in particular. To confirm the presence of a protein in a sample, at least one of the constituent peptides from that protein must be matched to a theoretical peptide sequence. The prediction of a fragmentation spectrum from a theoretical sequence so that it can be compared to an observed...
The use of neural networks as a nonlinear predictor in many applications including predictive image coding has been successfully presented by many researchers. However, almost all of the research papers have focused on the architecture of the neural network and very little attention has been given to the design of the training and testing data. This paper demonstrates how the choice of the training...
Grid has evolved dramatically into the era of service-oriented grid, which facilitates building of large-scale systems in standard fashions, reusability of essential functions, and interoperability among components. However, grid resource allocation is still a challenging problem for which a grid scheduler has to be operating in a dynamic and uncertain environment. Conventional scheduling algorithms...
We begin this paper by describing our rationale and overall design of an exceptional client model based on data mining algorithm. Then, we continue by summarizing the simulation details and describing the type of results obtained from implementing the proposed system, which consists of three heterogeneous data mining algorithms. The idea behind the model is that three heterogeneous data mining algorithms...
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...
Electricity demand forecasting is an important index to make power development plan and dispatch the loading of generating units in order to meet system demand. In order to improve the accuracy of the forecasting, we apply the feedforward neural network for electricity demand forecasting. Inspired by the idea of artificial fish swarm algorithm, in this paper we proposed one hybrid evolutionary algorithm...
Based on neural network, an improvement scheme that iterative matrix replace secondary derivative has been developed by introduced quasi-Newton algorithm. Profile code based on probability has been used and comparison of window width and learning training has been completed. The experiment results indicate that the prediction for secondary structures of protein obtain a very good effect based on neural...
A kind of adaptive PID control algorithm is analyzed, and the drawbacks of the existing algorithms are commented. As an improvement, a neural network intelligent control algorithm based on one-step prediction is developed. Result show that the new control method is more adaptable to the control of time-varying and nonlinear control systems.
According with control theory of variable-speed and constant-frequency (VSCF) pitch-controlled wind turbine, neural network algorithm is adopted to predict pitch angle at real-time working condition, and to obtain more accurate pitch angle reference value. It enhances control precision of the entire pitch-controlled system.
This paper uses constructive neural network learning approach to predict gas concentrations, under the framework of quotient space granular computing model. Using quotient space granular computing theory, the problem can be macro-level analysis - examining different particle size between the quotient space conversion, movement, interdependent relations, and the original features of the database information...
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