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The paper presents the design and analysis of some nonlinear and neural adaptive control strategies for a class of time-varying and nonlinear processes. In fact, a direct adaptive controller based on a radial basis function neural network used as online approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controllers design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics fermentation process, are included to illustrate the behaviour and the performance of the presented control laws.