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

Abstract

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.

Black-box modeling of nonlinear system using evolutionary neural NARX model

“…Evolutionary neural NARX (ENN) model is proposed in [21] by connecting the Multi-Layer Perceptron Neural Networks (MLP) model and NARX structure. The weight value of ENN model is optimized by a hybridizing modified differential evolution and backpropagation training (MDE-BP) algorithms.…”

“…Some suggestions on how to improve the DE performance on the training neural network have been studied [16]- [20]. Paper [21] proposed the hybrid modified differential evolution plus back-propagation algorithm (MDE-BP) for training neural network model. The performance and efficiency of the proposed MDE-BP training method are tested on identifying some benchmark nonlinear systems.…”

“…The performance and efficiency of the proposed MDE-BP training method are tested on identifying some benchmark nonlinear systems. However, paper [21] only perform for SISO nonlinear identification system. Therefore, this paper continuously studies the proposed MDE-BP training algorithm in [21] to train a neural network for a general nonlinear identification and its approach to model a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.…”

“…However, paper [21] only perform for SISO nonlinear identification system. Therefore, this paper continuously studies the proposed MDE-BP training algorithm in [21] to train a neural network for a general nonlinear identification and its approach to model a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system. These systems are suitable to test the effectiveness of the proposed method because of its hysteresis behavior and highly nonlinearity characteristic.…”

Conventional and non conventional body motions modelling and control. Application to the handwriting process

“…As for kinematic models, they generate curvilinear speed profiles reflecting the effect of neuromuscular impulses involved in the generation of motions. Many models have been developed under this approach such as the deltalognormal [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the double gaussian [30], the sigma-lognormal [31], the double beta [32]. The problem of kinematic models is the lack of information on the spatial aspect of the movement.…”