The phrase "a class of recurrent" is not complete and lacks context, making it difficult to determine its correctness in written English.
It could be used in contexts discussing categories or types of phenomena that repeat or occur regularly, but it needs additional information to clarify its meaning.
Example: "In our study, we identified a class of recurrent patterns in the data that suggested underlying trends."
Alternatives: "a category of recurring" or "a type of repeated".
In this paper, a class of recurrent neural networks with continuously distributed delays is discussed.
Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells.
This paper presents a class of recurrent neural network with HP memristors.
The state estimation problem is studied in this paper for a class of recurrent neural networks with time-varying delay.
This paper discusses the global output convergence of a class of recurrent neural networks with distributed delays.
In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays.
In this paper, the resilient state estimation problem is investigated for a class of discrete recurrent neural networks (RNNs) subject to mixed time-delays, missing measurements and stochastic disturbance.
In this paper, global exponential stability and periodicity of a class of reaction diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques.
This paper studies the problem of guaranteed cost control for a class of switched recurrent neural networks with interval time-varying delay.
This theoretical study and references 44 and 47 showed that, from a functional perspective, a certain class of recurrent networks can be considered as feedforward networks.
In this paper, we present a framework to construct a general class of recurrent neural networks (RNNs) as associative memories (AMs) for pattern storage and retrieval.
