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With the development of power industry, the proportion of Large-scale Generating Unit in power grid is getting bigger and bigger. The control object of the generating unit is a complicated manufacturing process which is strong-coupling, time-variable, nonlinear and big-lag. It is difficult to establish accurate model when the parameters of control object is uncertainty because of all disturbances,...
A novel approach is promoted for fuzzy neural ship controllers. A RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network...
Based on Object Linking and Embedding (OLE) for Process Control (OPC) technology, real-time data was collected and preprocessed by data filtering and anomaly detection methods. For ethylene cracking furnace's Multi-In-Multi-Out (MIMO) process, an online soft measurement model was built based on Radical Basis Functions (RBF) neural network. Meanwhile, an engineering method based on production experience...
By combining GA (Genetic Algorithm), which has the advantage of global optimization, and RBF, which has the advantage of local optimization, the calculation accuracy and convergence rate of the traditional RBF neural network are improved. So a combinational evaluation model is presented based on GA and RBF neural network. And it is applied to comprehensive analysis and evaluation of water quality,...
Hydroelectric generating unit system of water plant is a non-linear and complicated system. Conventional controller cannot get good controlling performance in control. In this study, an advanced soft computing technique based on fuzzy, neural network and genetic algorithm is used in the control of hydroelectric generating unit system of hydropower plant. Fuzzy reasoning system is used as controller...
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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