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An echo state network (ESN) is a particular sort of recurrent neural network that is designed to help engineers get the benefits of this network type, without some of the challenges in training other traditional types of recurrent neural networks. It is connected to the idea of reservoir computing, and the general philosophy of developing learning results from fixed random neurons.
In general, the echo state network deals with a random, large, fixed recurrent neural network where each neuron gets a non-linear response signal, and the connectivity and weights of neurons are fixed and assigned randomly. By dealing with input weights this way, the echo state network achieves a sort of flexible type of learning.
A model of a basic echo state network involves three components: an input signal, a dynamic reservoir and an output or “teacher” signal. Experts describe the work of this model as “harvesting reservoir states” and computing output weights to form machine learning analysis.
Essentially, different random states in the reservoir “echo” over time, and the network gets these interesting inputs and works on them to generate a certain “activation trajectory” then the strength of the network is its ability to generalize from these inputs with an input signal driving the reservoir model.
In a nutshell, the echo state network gets randomly assigned weights, so it is easy to train. The functionality is in the way that the network uses its inputs to generate learning results.