Each compatible combination contributes a product formed by multiplying the individual loop gains. These products appear in the determinant term with alternating signs, alongside terms for individual loops. Consequently, the formula accounts not only for separate feedback effects but also for their combined influence, allowing engineers to determine an overall transfer function directly from the signal-flow graph.
The node relationship determines whether loop gains can be combined in the determinant. Loops that do not share nodes may form a valid product term, whereas interacting loops must be treated differently. This distinction prevents incompatible feedback paths from being multiplied together and preserves the graph’s actual signal-flow structure during transfer-function analysis.
Non-touching loops can contribute joint gain products because their paths remain separate at the node level. Touching loops share at least one node, so they cannot be included in the same non-touching combination. Recognizing this difference is essential because the determinant’s terms must represent valid combinations of feedback paths rather than arbitrary products.
First, inspect the signal-flow graph to identify each individual loop and record its loop gain. Next, compare the loops node by node and group only those with no shared nodes. Multiply the gains within each valid group, then place the resulting terms, together with individual-loop terms, into the determinant using the required alternating signs.
The method is particularly useful when a control or communication diagram contains several forward paths and feedback paths that make direct algebra cumbersome. A signal-flow graph exposes the relevant paths and loop combinations explicitly, so engineers can calculate the overall transfer function without repeatedly reducing complex diagram sections into lengthy algebraic expressions.
Loop analysis shows how separate feedback paths and their valid combinations influence signal transmission and system response. In control-system studies, that structure supports analysis of the overall transfer function and stability-related behavior. In communication systems, it helps organize interacting signal paths, making the graph’s feedback relationships easier to interpret.