Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control essential. The complex agro-hydrological system dynamics, however, often pose challenges in applications. In this work, we propose a two-layer neural network (NN) framework approximate the dynamics of system. To minimize prediction error, linear bias correction added propos...

Modular multilevel converter (MMC) has attracted much attention for years due to its good performance in harmonics reduction and efficiency improvement. Model predictive control (MPC) based controllers are widely adopted MMC because the design is straightforward different objectives can be simply implemented a cost function. However, computational burden of MPC imposes limitations implementatio...

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algo...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has...

this paper employs nonlinear disturbance observer (ndo) for robust trajectory-free nonlinear model predictive control (nmpc) of biped robots. the ndo is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. in contrary to the slow-varying disturbances, handling sudden pushing ...

One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes proce...