Dynamical Networks (miniporject) Effect of Topology of the Reservoir on Performance of Echo State Networks
نویسنده
چکیده
In this report we studied the effect of reservoir topology on the learning performance of the echo state networks. Different tasks with different properties were defined and three general models (scale-free, small-world and random) were considered for reservoirs. Some relations between the topology-related parameters of the reservoirs and performance of it (mean square error) was discovered. For memoryless and simple tasks it seems graphs with low average degree performed better for scalefree and small-world networks. In contrary, for complex tasks that need memory, networks with slightly higher average degree performed better. In average it was concluded that for same network size and same average degree (same number of links), random reservoirs and small-world reservoirs performed almost the same but better than scale-free reservoirs.
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