So sánh phương pháp

	Echo State Network	LSTM	Mạng nơ-ron hồi quy
Lĩnh vực	Học sâu	Học sâu	Học sâu
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2004	1997	1986–1990
Người khởi xướng	Herbert Jaeger & Harald Haas	Hochreiter, S. & Schmidhuber, J.	Rumelhart, D. E.; Elman, J. L.
Loại	Recurrent neural network with fixed random reservoir	Recurrent neural network (gated memory cell)	Sequential neural network
Công trình gốc	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Tên gọi khác	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	RNN, Elman network, Jordan network, simple recurrent network
Liên quan	3	5	3
Tóm tắt	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.