Compară metode

	Rețea cu Stare de Ecou (Echo State Network)	LSTM
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2004	1997
Autorul original	Herbert Jaeger & Harald Haas	Hochreiter, S. & Schmidhuber, J.
Tip	Recurrent neural network with fixed random reservoir	Recurrent neural network (gated memory cell)
Sursa seminală	Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78–80.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Denumiri alternative	ESN, Liquid State Machine (related formulation), Reservoir Computing, Yankı Durum Ağı	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
Înrudite	3	5
Rezumat	An Echo State Network (ESN) is a type of recurrent neural network introduced by Herbert Jaeger and Harald Haas in 2004 that exploits a large, randomly connected, fixed recurrent layer — the reservoir — to project input signals into a high-dimensional nonlinear space. Only the linear output weights are trained, typically via ridge regression, making ESNs computationally inexpensive yet highly expressive for temporal and chaotic time-series modeling tasks.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.