How can compose fuzzy rules in 1-D and 2-D?

Question

fuzzy implication function based on the interpretation "A entails B" can be expressed as : $$R_{s}=A \longrightarrow B=\int_{X\times Y} sgn[\mu_{B}(y)-\mu_{A}(x)]/(x,y),$$ or, alternatively, $$f_{s}(a,b)=sgn(b-a)=\begin{cases} 1 &\mbox{if } b\geq a \\ 0 & \mbox{if } otherwise, \end{cases} $$ where $a=\mu_{A}(x)$ and $b=\mu_{B}(y)$ and $\mu_{A}(x)=\mu_{B}(x)=bell(x,4,3,10)$ and $z=f_{s}(a,b)$ and $(\mu_{Rs}(x,y)= sgn[\mu_{B}(y)-\mu_{A}(x)])$. I want using above identities to show the fuzzy rule "if x is A or y is B then z is C" which is equivalent to the union of two fuzzy rules "if x is A then z is C" and "if y is B then z is C" under max-min composition. I would be grateful if anyone help to solve this problem? How can I obtain $\mu_{C}(z)$ and how can I obtain $\mu_{R}(x,z)$, $\mu_{R}(y,z)$? I would be grateful if anyone help to solve this problem?