An Improved Fuzzy Neural Network Compound Control Scheme for Inertially Stabilized Platform for Aerial Remote Sensing Applications

Zhou, Xiangyang; Li, Yating; Yuan, Jia; Zhao, Libo

doi:10.1155/2018/7021038

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…According to the above parameter settings, assume that T ik represents the level specified by F k and 1 is the highest level, the higher the value, the lower the level [35]- [37]. T k represents the ultimate unified level of the k field k ∈ {1, 2, .…”

Section: Preliminary Operations For Medical Data Miningmentioning

confidence: 99%

Medical Data Stream Distribution Pattern Association Rule Mining Algorithm Based on Density Estimation

Wang

Dong

2019

IEEE Access

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The traditional data mining method is featured by no analysis over the data distribution and incomplete derived association rule. As a result, the data mining results have the deficiencies of large redundancy probability, large root-mean-square error of approximation (RMSEA) and long consumption time. To handle these issues, this paper proposes a medical data stream distribution pattern association rule mining algorithm based on density estimation. This paper collects medical data, selects the distance method to detect abnormal orphan data in the data stream, detects the duplicate data in the data stream by the similar field matching degree, and eliminates the abnormal data and the duplicate data. Then, the data stream density is estimated based on the histogram estimation samples. According to the data density estimation results, this paper analyzes the distribution of medical data stream from perspectives of concentration, dispersion and morphological characteristics of data distribution. Afterwards, the data distribution pattern association rule mining model is constructed based on compound neural network, data distribution parameters are entered into model's clustering layer, and in-depth training is conducted over the BP (Back Propagation) neural network at the model's mining layer. Meanwhile, all rules under the combination of hidden layer's neuron activity value and corresponding output value, and all rules under the combination of hidden layer's neuron activity value and corresponding input value are derived, so as to complete association rule mining of medical data stream distribution pattern. The experimental results show that the proposed algorithm has a contour curve closest to the true probability density curve; the dispersion degree of medical data is within a reasonable range, and the medical data has high stability; the data redundancy probability is smaller; the mining result's RMSEA is small; data mining takes less time.INDEX TERMS Compound neural network, medical data, density estimation, distribution pattern, association rules, mining.

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Section: Preliminary Operations For Medical Data Miningmentioning

confidence: 99%

Medical Data Stream Distribution Pattern Association Rule Mining Algorithm Based on Density Estimation

Wang

Dong

2019

IEEE Access

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“…, and the output layer consists of three nodes, i.e., one position and two orientations parameter X = z, α, β [27,28]. The number of the nodes (k) in the hidden layer is generally determined by empirical formulas [29], that is,…”

Section: Forward Kinematics Of the Bp Neural Networkmentioning

confidence: 99%

Forward Kinematics and Workspace Determination of a Novel Redundantly Actuated Parallel Manipulator

Zhang

Fang

Zhang

et al. 2019

International Journal of Aerospace Engineering

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This paper presents a novel redundantly actuated 2RPU-2SPR parallel manipulator that can be employed to form a five-axis hybrid kinematic machine tool for large heterogeneous complex structural component machining in aerospace field. On the contrary to series manipulators, the parallel manipulator has the potential merits of high stiffness, high speed, excellent dynamic performance, and complicated surface processing capability. First, by resorting to the screw theory, the degree of freedom of the proposed parallel manipulator is briefly addressed with general configuration and verified by Grübler-Kutzbach (G-K) criteria as well. Next, the inverse kinematics solution for such manipulator is deduced in detail; however, the forward kinematics is mathematically intractable. To deal with such problem, the forward kinematics is solved by means of three back propagation (BP) neural network optimization strategies. The remarkable simulation results of the parallel manipulator demonstrate that the BP neural network with position compensation is an appropriate method for solving the forward kinematics because of its various advantages, such as high efficiency and high convergence ratios. Simultaneously, workspaces, including joint space and workspace of the proposed parallel manipulator, are graphically depicted based on the previous research, which illustrate that the proposed manipulator is a good candidate for engineering practical application.

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“…Intelligent control mainly studies nonlinear, time-varying, uncertainty, and objects that are difficult to establish an accurate mathematical model. Therefore, some scholars have applied fuzzy control [9,10,12] , neural network [13,14] , genetic algorithm [15] , particle swarm optimization [16,17] , and other methods on the control of the gimbal. Intelligent control algorithm combined with the advantages of artificial intelligence can deal with many complex nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Sliding-Mode Control of Three-Axis Stabilization Gimbal

Yan

Shi

et al. 2020

IEEE Access

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Aiming at the attitude control problem of three-axis stabilization gimbal, an algorithm combining interval type-2 fuzzy control and sliding-mode control was designed. Firstly, the mathematical model of the three-axis stabilization gimbal actuator was established after analyzing the mechanical structure of the three-axis stabilization gimbal. Then, a sliding-mode controller was designed based on the position ring of the actuator of three-axis stabilization gimbal. An interval type-2 control was used to reduce chattering in sliding-mode control. The switching function and its derivative are taken as the input of fuzzy control, and the rate of change of the approach law is taken as the output of fuzzy control. Fuzzy control can adjust the gain of sliding-mode surface dynamically, enhance the adaptability on external random disturbance, improve the speed of convergence and weaken chattering. The simulation results show that the output of the controller designed in this paper is more stable, and the tracking of the three-axis stabilization gimbal is more rapid and accurate.INDEX TERMS Interval type-two fuzzy control; sliding-mode control; three-axis Stabilization gimbal; position ring

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An Improved Fuzzy Neural Network Compound Control Scheme for Inertially Stabilized Platform for Aerial Remote Sensing Applications

Cited by 9 publications

References 38 publications

Medical Data Stream Distribution Pattern Association Rule Mining Algorithm Based on Density Estimation

Medical Data Stream Distribution Pattern Association Rule Mining Algorithm Based on Density Estimation

Forward Kinematics and Workspace Determination of a Novel Redundantly Actuated Parallel Manipulator

Interval Type-2 Fuzzy Sliding-Mode Control of Three-Axis Stabilization Gimbal

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