And the name denotes the connection.
The white arrow denotes the connection between the superficial layer of the PLL and the dura [5].
The random variable Q, which denotes the connection termination time, is assumed to be geometrically distributed with mean 1/(1−λ)[32].
In this study each node denotes a unique IP address and each edge denotes the connection between one IP address to another.
The constant c i j denotes the connection strength of the j th neuron on the i th neuron at time t − τ j ( t ).
Here, λ ij denotes the state transition time from state i to j and μ ij denotes the connection lifetime from state i to j where 0 ≤ i,j ≤ 7 (max number of networks).
C and D denote the connection weight matrices with ((ntimes n)) dimensions.
A j = diag ( a ¯ 1 j, a ¯ 2 j, …, a ¯ n j ), a ¯ i j > 0 represents the self-feedback term; W 0 j, W 1 j denote the connection weights, the discretely delayed connection weights, and the distributively delayed connection weight, respectively.
A j = diag ( a ¯ 1 j, a ¯ 2 j, …, a ¯ n j ), a ¯ i j > 0, represents the self-feedback term; B j ∈ R n × m are control input matrices; W 0 j, W 1 j denote the connection weights and the delayed connection weights, respectively.
where, corresponds to the number of units in a neural network, is the state variable of the th neuron, denotes the passive delay rates,, denote the connection weights of the unit on the unit,, are the activation functions of the neurons, is the input of the unit, and is the transmission delay of the th neuron such that,,,, where, and are some constants.
c i j, p i j, r i j, q i j, w i j, d i j, S i j, T i j, ζ j i, λ j i, ρ j i, π j i, σ j i, h j i, M j i, M j i denote the connection weights.
