Research on RBF neural network model reference adaptive control system based on nonlinear U – model

The overall objective of this study is to design the nonlinear U-model-based radial basis function neural network model reference adaptive control system, through research into a class of complex time-varying nonlinear plants. First, the ideal nonlinear plant is adopted as the reference model and transformed into the U-model representation. In the process, the authors establish the corresponding relationship between the degrees of the reference nonlinear model and the controlled nonlinear plants, and carry out research into the corresponding coefficient relationship between the reference nonlinear model and the controlled nonlinear plants. Also, the impact of the adjusting amplitude and tracking speed of the model on the system control accuracy is analyzed. Then, according to the learning error index of the neural network, the paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety. With the weight coefficients and network parameters automatically updated and the adaptive controller adjusted, the output of controlled nonlinear plants can track the ideal output completely. The simulation results show that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive control system based on the gradient descent method

Research on parallel nonlinear control system of PD and RBF neural network based on U model

The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system

Research on parallel control of CMAC and PD based on U model

In this paper, the nonlinear U model with time-varying coefficients is investigated and the transformation of the nonlinear model is accomplished by the Newton iterative algorithm. Based on the nonlinear U model, a control algorithm with cerebellar model articulation controller and proportional derivative (PD) in parallel is proposed. The algorithm learns online through a neural network while optimizing the output of the PD, which ultimately enables the actual output of the system to track up to the desired output. Considering that the nonlinear object has the characteristic of rapid change with time, the article improves the PD algorithm to nonlinear PD control algorithm to complete the design of the system. The algorithm automatically adjusts the weights according to the error magnitude to complete the controller parameter adjustment, thus reducing the error of the system. The simulation results show that the nonlinear PD algorithm is better than the PD algorithm, meanwhile, the tracking speed and control precision of the system are improved

Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification

Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results

Effects of Antibacterial Peptide Extracted from Bacillus subtilis fmbJ on the Growth, Physiological Response and Disease Resistance of Megalobrama amblycephala

The effects of an antibacterial peptide obtained from Bacillus subtilis fmbJ on growth, serum lysozyme complements 3 and 4, total protein content, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total antioxidative capacity, superoxide dismutase (SOD) activity, malondialdehyde (MDA) content, and disease resistance of Wuchang bream (Megalobrama amblycephala) were examined. Fish were randomly divided into five groups: a control group which was fed a basic diet, and four groups fed the basic diet supplemented with 0.1%, 0.2%, 0.4%, or 0.8% antibacterial peptide. At eight weeks, M. amblycephala fed the diet containing 0.2% antibacterial peptide had higher serum lysozyme activity, complement 3 and 4 contents, and SOD activity than the control fish, but lower serum MDA content and AST activity. Fish fed the 0.4% diet had higher weight gain rate, serum lysozyme activity, complement 4 content, total antioxidative capacity, and total protein than the control, and lower serum ALT activity. Feed conversion ratios of fish fed the 0.2% or 0.4% diets were lower than those of control fish. Artificial infection with Aeromonas hydrophila resulted in 93% cumulative mortality in the control group, and 61-84% in the groups fed the 0.2% or 0.4% diets. The present study suggests that feed supplementation with 0.2-0.4% antibacterial peptides can stimulate immunity, increase resistance to pathogenic infection, and promote growth in M. amblycephala

OMAE2010-21069 TEST AND ANALYSIS OF VIBRATION CHARACTERISTIC ON NEW TYPE DYNAMIC HYDROCYCLONE

ABSTRACT Dynamic hydrocyclone is currently used in separating oil and water from the crude oil, in which the fluid rolling motion is drived by the external power. Compared with the static type, the dynamic hydrocylone has higher separating property, while its structure is more complex and its separating property is influenced seriously by all the rotary components. Based on the original model, dynamic hydrocyclone of the new type is designed and manufactured, while applying the vibration signal collection and analysis system of IOtech640 type in the vibration characteristic analysis of the model body. The result shows that, when the rotating speed rises from 600r/min to 2000r/min and the flux from 1 to 3 m 3 /h, the level time-domain vibration peak of the monitoring site both the near the electromotor and the faraway is under 3.4×10 -4 , while the vertical is under 3.2×10 -4 , with steady frequency components in the vibration signal. It is illustrated that vibration intensity of the dynamic hydrocyclone of the new optimized type is lightened, which may confirm the improvement of the separation property and the operational life

OMAE2010-21049 ANALYSIS OF EROSION AND FAILURE IN THE SUDDEN EXPANSION FRACTURING TUBING OF DEEP GAS WELLS

ABSTRACT With the increasing of flow rate during fracturing in deep gas well, the erosion of fracturing tubing is an issue of immense concern to the industry. Based on the Euler-Euler two -fluid theory, the numerical simulations have been performed to predict the flow field in the sudden expansion fracturing tubing. The velocity distributions and sand concentration profiles are obtained, and the simulation results show that separation and reflux come into being in the sudden expansion fracturing tubing when pumping sand slurries at high rate, and the sand concentration increases at some regions. The erosion and failure of the fracturing tubing are relevant to the sand concentration, the velocity and the impact angle. The erosion model was established with the erosion experiment, and the numerical simulation results were used to describe the erosion rate of sudden expansion fracturing tubing according to the established erosion models. The mainly erosion region obtained through the simulation is basically agree with the failure region of tubing during fracturing in deep gas wells

Gene Expression Profiles Deciphering Rice Phenotypic Variation between Nipponbare (Japonica) and 93-11 (Indica) during Oxidative Stress

Rice is a very important food staple that feeds more than half the world's population. Two major Asian cultivated rice (Oryza sativa L.) subspecies, japonica and indica, show significant phenotypic variation in their stress responses. However, the molecular mechanisms underlying this phenotypic variation are still largely unknown. A common link among different stresses is that they produce an oxidative burst and result in an increase of reactive oxygen species (ROS). In this study, methyl viologen (MV) as a ROS agent was applied to investigate the rice oxidative stress response. We observed that 93-11 (indica) seedlings exhibited leaf senescence with severe lesions under MV treatment compared to Nipponbare (japonica). Whole-genome microarray experiments were conducted, and 1,062 probe sets were identified with gene expression level polymorphisms between the two rice cultivars in addition to differential expression under MV treatment, which were assigned as Core Intersectional Probesets (CIPs). These CIPs were analyzed by gene ontology (GO) and highlighted with enrichment GO terms related to toxin and oxidative stress responses as well as other responses. These GO term-enriched genes of the CIPs include glutathine S-transferases (GSTs), P450, plant defense genes, and secondary metabolism related genes such as chalcone synthase (CHS). Further insertion/deletion (InDel) and regulatory element analyses for these identified CIPs suggested that there may be some eQTL hotspots related to oxidative stress in the rice genome, such as GST genes encoded on chromosome 10. In addition, we identified a group of marker genes individuating the japonica and indica subspecies. In summary, we developed a new strategy combining biological experiments and data mining to study the possible molecular mechanism of phenotypic variation during oxidative stress between Nipponbare and 93-11. This study will aid in the analysis of the molecular basis of quantitative traits

New fusion transcripts identified in normal karyotype acute myeloid leukemia

Genetic aberrations contribute to acute myeloid leukemia (AML). However, half of AML cases do not contain the well-known aberrations detectable mostly by cytogenetic analysis, and these cases are classified as normal karyotype AML. Different outcomes of normal karyotype AML suggest that this subgroup of AML could be genetically heterogeneous. But lack of genetic markers makes it difficult to further study this subgroup of AML. Using paired-end RNAseq method, we performed a transcriptome analysis in 45 AML cases including 29 normal karyotype AML, 8 abnormal karyotype AML and 8 AML without karyotype informaiton. Our study identified 134 fusion transcripts, all of which were formed between the partner genes adjacent in the same chromosome and distributed at different frequencies in the AML cases. Seven fusions are exclusively present in normal karyotype AML, and the rest fusions are shared between the normal karyotype AML and abnormal karyotype AML. CIITA, a master regulator of MHC class II gene expression and truncated in B-cell lymphoma and Hodgkin disease, is found to fuse with DEXI in 48% of normal karyotype AML cases. The fusion transcripts formed between adjacent genes highlight the possibility that certain such fusions could be involved in oncological process in AML, and provide a new source to identify genetic markers for normal karyotype AML