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In this paper an efficient Q-learning paradigm implemented on a fuzzy CMAC network is proposed. The fuzzy CMAC network topological architecture is described. The continuous states of the system are partitioned into a number of fuzzy boxes. With the proposed fuzzy CMAC the Q-values of agents in the fired fuzzy boxes are evaluated and the control actions with maximum Q-values can be derived. The proposed hybrid adaptive and learning type of Fuzzy Neural control system based on the Q-learning is applied to the control of a pH-neutralization process.