Nonrecurrentlike

Nonrecurrentlike is an adjective used to describe phenomena, models, or sequences that do not exhibit recurrence in a formal sense, or that resemble non-recurrent behavior. The term is not widely standardized and is typically used descriptively rather than as a formal classification. Its exact meaning depends on the context.

In mathematics and dynamical systems, nonrecurrent-like behavior refers to trajectories or sequences that do not return arbitrarily close to previously visited states. Examples include strictly monotonic sequences or systems that move away from prior regions of phase space and do not cycle.

In machine learning and data processing, nonrecurrent-like architectures refer to models that do not use recurrent connections across time steps, such as feedforward networks when processing sequential data, or models that rely on attention or convolution without persistent hidden state. The label is primarily descriptive and can indicate that memory of past inputs is not carried across time steps in a given model.

Usage notes: the phrase is informal and context-dependent. It should not be assumed to denote a formal subclass in math or ML.

See also:

- Recurrence

- Non-recurrence

- Recurrent neural network

- Feedforward neural network

- Dynamical systems

- Time series