Abstract:  In this paper, two self-scaling symmetric rank one algorithms  with projection are proposed for solving nonlinear monotone equations. In the two algorithms, a simple  rule in choosing parameters in symmetric rank one is modified and  a cautious update rule is used. Under the condition that the nonlinear  monotone function is Lipschitz  continuous, the global convergence of these two algorithms is proved.  Compared with the same type BFGS algorithms, some preliminary numerical  experiments  are also done. The results indicate that the numerical performance of   SSR1 class algorithms may be competitive with that of the counterparts.