A Novel Method for the Evolution of Artificial Neural Networks

نویسندگان

  • SPIRIDON D. LIKOTHANASSIS
  • EFSTRATIOS F. GEORGOPOULOS
چکیده

Evolving Artificial Neural Networks (ANN) is a new method that, except of the training, was applied to the structure optimization problem. This method combines ideas from both the evolution and adaptive signal processing techniques. An ANN is considered as a layered array of non-linear systems (the neuron models), each producing on its output a local error. Each of these errors is minimized using the Extended Kalman Filter (EKF). Then the evolutionary algorithm is used to search for the best array that minimizes the global error on the network’s output. The initial population is randomly created consisting of ANN with different structure (in the hidden layer). The proposed algorithm has been tested with two different real world applications giving very promising results. Key-Words: Artificial Neural Networks, Genetic Algorithms, Evolutionary Algorithms, Kalman Training, Multiple Extended Kalman Filter, Architecture Optimization, Neural Network Training CSCC'99 Proc.pp.3141-3146

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تاریخ انتشار 1999