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This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

This study provides a novel quad-function-link network to adjust the lower and upper weights of a wavelet interval type-2 fuzzy brain emotional structure to increase the response and performance for the synchronization of 3D nonlinear chaotic systems. The proposed control system is a hybrid method that comprises a new wavelet interval type-2 fuzzy quad-function-link brain emotional controller and a robust controller. It contains a fuzzy inference system and three substructures with five layers. The substructures are an amygdala, a prefrontal cortex, and a novel quad-function-link network that can adjust the weights efficiently for the amygdala and prefrontal cortex networks to achieve the synchronization of the master–slave systems well with reduced tracking errors. Then, a Lyapunov stability function is employed to provide the adaptive laws, and they are effectively used online to adjust the system parameters. Finally, simulation studies of two 3D nonlinear chaotic systems are used to verify the superiority and advantage of the proposed algorithm.

It is essential to conduct the quality control for gauranteeing sound products after finishing conventional manufacturing processes. Vision-based inpection system has been extensively applied to various industries linked with concept of the smart factory since it does not only enhance the inspecting accuracy, but also decrease the cost for the human inspection, substantially. This paper mainly concerns the development of the inspecting system for the casting products with supported by the convolutional neural network, which makes it possible to detect various types of defects such as blow hole, chipping, crack, and wash automatically. To obtain high accuracy in inspecting system, it does not only require sub-partitioning of the original images, but also apply multiple labeling according to the order of the sub-images and the existence of the defects. Performance of the proposed inspecting algorithm has been validated with the 400 casting products, in which it exhibits substantially high accuracy more than 98%, experimentally.

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC = 0.944) and validation dataset (AUC = 0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.

We investigate theoretically the nature of the states and the localization properties in a one-dimensional Anderson model with long-range correlated disorder and weak nonlinearity. Using the stationary discrete nonlinear Schrodinger equation, we calculate the disorder-averaged logarithm of the transmittance and the localization length in the fixed input case in a numerically exact manner. Unlike in many previous studies, we strictly fix the intensity of the incident wave and calculate the localization length as a function of other parameters. We also calculate the wave functions in a given disorder configuration. In the linear case, flat phased localized states appear near the bottom of the band and staggered localized states appear near the top of the band, while a continuum of extended states appears near the band center. We find that the focusing Kerr-type nonlinearity enhances the Anderson localization of flat phased states and suppresses that of staggered states. We observe that there exists a perfect symmetry relationship for the localization length between focusing and defocusing nonlinearities. Above a critical value of the strength of nonlinearity, delocalization due to the long-range correlations of disorder is destroyed and all states become localized.

By making use of a panel dataset that covers 61 provinces of Vietnam over the period 1997 to 2006, this paper examines the link between financial development and economic growth. Our analysis, which is based on endogenous growth theory, reveals that financial development has contributed to economic growth in Vietnam. We find that high ratio of credit to Gross Provincial Product (GPP) has accelerated economic growth in Vietnam. We also found a strong positive link between financial development and economic growth when alternative measures of financial development were used. The impact of foreign direct investment on economic growth will be stronger if more resources are invested in financial market development.