In this paper, we propose a transmitter structure in digital QAM systems where pre-compensator compensates for nonlinearity with "memory effects" at the output amplifier. The nonlinearity is modeled as a linear time-invariant filter cascaded by memoryless nonlinearity (Wiener model), whereas the pre-compensator comprises an FIR-type adaptive filter that follows a memoryless predistorter based on a series expansion with orthogonal polynomials for digital QAM data. The predistorter and the adaptive filter of the pre-compensator are stochastically and directly adapted using the error signal. The theoretically optimum parameters of the predistorter are approximately solved whence the steady-state mean square compensation error is calculated. Simulations show that the proposed pre-compensator can be adapted to achieve a sufficiently small compensation error, restoring the original QAM constellation through linearization and equalization of the nonlinearity with memory effects.