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In order to obtain the law of the building settlement and forecast it effectively, neural network model was established for building settlement forecasting based on measured data, and an engineering example is shown to test and verify. Firstly, data of building settlement measured were normalized; embedding dimension was selected to establish the leaning samples. Mean square error (MSE) and mean absolute...
In wastewater treatment plants, It's difficult to acquire online data of BOD5 (Biochemical Oxygen Demand for 5 days) due to its characteristic and unreliability of on-line sensors. Furthermore, although soft sensors models are widely used in wastewater treatment, only a few approaches for soft sensors models are designed to address the problems currently existing in the wastewater treatment. In such...
In this paper, a new prediction model, based on chaos theory and BP artificial neural network, is developed to predict the risk of credit card transactions. Embedding dimension of phase-space reconstruction is used to determine network structure, and overcomes the dependence on large amount of samples. Experiments shows that the method based on combination of chaos theory and neural network can improve...
Effluent ammonia-nitrogen (NH3-N), chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing approach based on the back propagation (BP) neural networks and fuzzy-rough sets (FR-BP) has been applied for forecasting effluent NH3-N, COD and TN...
In order to further improve the accuracy of short-term load forecasting, a selective neural network ensemble method using discrete differential evolution algorithm is proposed. Firstly, the individual vectors in differential evolution algorithm are dispersed. Secondly, a group of RBF neural networks with larger difference are trained independently and a binary bit string in multi-dimensional space...
When combining grey system with RBF neural network, local optimization and convergence problems are still existed, so genetic algorithm is introduced to assist the modeling of grey neural network in this paper. Firstly, genetic algorithm is employed to solve the parameters of improved GM(1,1) with Lagrange's value theorem, and then RBF neural network is parallel connected to compensate errors. A new...
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