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This paper applies the principle of the immune system adjustment to optimize the structure parameters of wavelet network, so as to establish a new type of wavelet neural network model which will be applied to turbine exhaust steam enthalpies. The calculation results show that the model has fast convergence, simple operation, high accuracy in forecasting, and has certain value of engineering applications.
This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar...
Based on clonal selection theory, an adaptive parallel immune evolutionary strategy (PIES) is presented. Compared with conventional evolutionary strategy algorithm (CESA) and immune monoclonal strategy algorithm (IMSA), experimental results show that PIES is of high efficiency and can effectively prevent premature convergence. A three-layer feed-forward neural network is presented to predict state...
This paper deals with load optimal dispatching among power plant units in the context of electric power market competition environment. The multi-agent evolutionary algorithm (MAEA) is specially useful in load optimal dispatching, the model of which is set up based on characteristic curve of the generator coal consumption approached by quadratic function. In particular, the following restraints are...
This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state model using a static neural network and the front is estimated using various geometric and heuristic approaches. This paper is the first of the two stages to eliminate the need for a human...
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