In this article, we propose centralized and distributed continuous-time penalty methods to find a Nash equilibrium for a generalized noncooperative game with shared inequality and equality constraints and private inequality constraints that depend on the player itself. By using the <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> penalty function, we prove that the equilibrium of a differential inclusion is a normalized Nash equilibrium of the original generalized noncooperative game, and the centralized differential inclusion exponentially converges to the unique normalized Nash equilibrium of a strongly monotone game. Suppose that the players can communicate with their neighboring players only and the communication topology can be represented by a connected undirected graph. Based on a leader-following consensus scheme and singular perturbation techniques, we propose distributed algorithms by using the exact <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> penalty function and the continuously differentiable squared <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> penalty function, respectively. The squared <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> penalty function method works for games with smooth constraints and the exact <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> penalty function works for certain scenarios. The proposed two distributed algorithms converge to an <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula>-neighborhood of the unique normalized Nash equilibrium and an <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula>-neighborhood of an approximated Nash equilibrium, respectively, with <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> being a positive constant. For each <inline-formula><tex-math notation="LaTeX">$\eta >0$</tex-math></inline-formula> and each initial condition, there exists an <inline-formula><tex-math notation="LaTeX">$\varepsilon ^*$</tex-math></inline-formula> such that for each <inline-formula><tex-math notation="LaTeX">$0< \varepsilon < \varepsilon ^*$</tex-math></inline-formula>, the convergence can be guaranteed where <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is a parameter in the algorithm.