Dissipative criteria for Takagi–Sugeno fuzzy Markovian jumping neural networks with impulsive perturbations using delay partitioning approach

Advances in Continuous and Discrete Models

Theory and Modern Applications

Table 3 Allowable upper bound of \(\tau _{2}\) and d for different \(\tau _{1}\) and μ (\(\mu _{1}=\mu _{2}\))

\(\tau _{1}\)	μ = 0.1	μ = 0.3	μ = 0.5	μ = 0.7	μ = 0.9
\(\tau _{1}=0\)	\(\tau _{2}=0.5806\)	\(\tau _{2}=0.5369\)	\(\tau _{2}=0.5324\)	\(\tau _{2}=0.5296\)	\(\tau _{2}=0.5294\)
\(\tau _{1}=0\)	d = 0.5264	d = 0.5036	d = 0.5031	d = 0.5028	d = 0.5025
\(\tau _{1}=0.2\)	\(\tau _{2}=0.6049\)	\(\tau _{2}=0.5687\)	\(\tau _{2}=0.5538\)	\(\tau _{2}=0.5525\)	\(\tau _{2}=0.5506\)
\(\tau _{1}=0.2\)	d = 0.6588	d = 0.6459	d = 0.6426	d = 0.6415	d = 0.6412
\(\tau _{1}=0.4\)	\(\tau _{2}=0.7834\)	\(\tau _{2}=0.7426\)	\(\tau _{2}=0.7354\)	\(\tau _{2}=0.7328\)	\(\tau _{2}=0.7321\)
\(\tau _{1}=0.4\)	d = 0.6744	d = 0.6703	d = 0.6693	d = 0.6685	d = 0.6680