Abstract: By syncretizing the global design and local design of feedforward neural networks, this paper proposes a localized multi-layer Perceptron (LMLP), which is constructed by a set of distributively associated subnets with the same structure. Simulation results show that this network has strong learning capacity for complex nonlinear systems, and is applicable for real-time learning control, by using either direct inverse modeling or distal learning strategy.