The issue of topology choosing in artificial neural networks for chaotic time series forecasting is studied. Motivated by the growth and development process of cerebral cortex
an algorithm for constructing cortex-like neural network
complex echo state network(CESN)
is proposed. Some factors in both the distance-dependent growth mechanism and time-window-dependent growth mechanism
which have effects on the process of the growth and development of the cortex networks
are analogously defined as construction parameters that controls the process in creating a CESN
such as the size factor
the distance-sensitive factor
the density-sensitive factor
the seed-neuron number and the time-window width
etc. It is found from experiments that the topological characters of a CESN can be uniquely determined by these parameters
and that when the seed-neuron number is set to be about 10% of the whole network size
the created CESN can have almost the best forecasting performance. Simulation results show that CESNs can be created with less time-consumption and better forecasting performance. It can be concluded that the proposed algorithm is a more efficient network constructing method.
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references
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