In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the breeder genetic algorithms are considered to provide the optimal set of synapt...
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ISBN:
(纸本)3540650784
In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the breeder genetic algorithms are considered to provide the optimal set of synaptic weights of the network:. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with geneticalgorithms and Back Propagation learning technique is performed.
This paper wishes to describe Evolutionary algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying Evolutionary algorithms are outlined...
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This paper wishes to describe Evolutionary algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying Evolutionary algorithms are outlined. Several versions of Evolutionary algorithms are briefly described, focussing on their similarities and on their differences as well. Then their application to both Direct and Inverse Aerofoil Design Problem is described, and results are given. Finally, several possible parallel models for Evolutionary algorithms are discussed, and the results of the application of one of them to the above problem are presented.
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