A neural differential evolution identification approach to nonlinear systems and modelling of shape memory alloy actuator

This paper proposes a hybrid modified differential evolution plus back‐propagation (MDE‐BP) algorithm to optimize the weights of the neural network model. In implementing the proposed training algorithm, the mutation phase of the differential evolution (DE) is modified by combining two mutation strategies rand/1 and best/1 to create trial vectors instead of only using one mutation operator or rand/1 or best/1 as the standard DE. The modification aims to balance the global exploration and local exploitation capacities of the algorithm in order to find potential global optimum solutions. Then the local searching ability of the back‐propagation (BP) algorithm is applied in that region so as to swiftly converge to the optimum solution. The performance and efficiency of the proposed method is tested by identifying some benchmark nonlinear systems and modeling the shape memory alloy actuator. The proposed training algorithm is compared with the other algorithms, such as the traditional DE and BP algorithm. As a result, the proposed method can improve the accuracy of the identification process.