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It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and the operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks. A hysteresis switching algorithm is applied to the new identification approach and the convergence of the identifier is proved. Adaptive controller also has a multi-model structure. We consider three different architectures of the multi-model neuro control. The simulation results show that the multiple neuro controllers have better performances for the pH neutralization process.