Self-organizing CMAC neural networks and adaptive dynamic control

Hu, Jianjuen; Pratt, Gill A.

doi:10.1109/isic.1999.796665

Cited by 21 publications

(5 citation statements)

References 9 publications

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“…However, in practice, the micromanipulation of cells is very sensitive to environmental disturbances, such as stochastic dynamics caused by equipment vibration, liquid flow and random Brownian motion [40]. The interference term is recorded as d ∈ R 2×1 and substituted into Equation (33). The actual cell dynamics model is shown as Equation ( 34):…”

Section: Cell Dynamics Modelmentioning

confidence: 99%

“…In the study of multicellular cooperative control, by extending Equation (33), the dynamics of the ith trapped cell is expressed as:…”

Section: Cell Manipulation Control Systemmentioning

confidence: 99%

“…The size of the storage space is difficult to determine, which will affect the learning effect of the network. Therefore, some FCMNNs with self-organizing characteristics are proposed [33][34][35]. However, these FCMNNs have problems such as complex methods, a weak ability to adapt to online real-time, and no analysis of structure deletion.…”

Section: Introductionmentioning

confidence: 99%

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Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network

et al. 2022

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Holographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar model neural network (NSOFCMNN) is proposed and applied to the cell manipulation control of holographic optical tweezers. This control system consists of a main controller using the NSOFCMNN with a new self-organization mechanism, a robust compensation controller, and a higher order sliding mode. It can accurately move the captured cells to the expected position through the optical trap generated by the holographic optical tweezers system. Both the layers and blocks of the proposed NSOFCMNN can be adjusted online according to the new self-organization mechanism. The compensation controller is used to eliminate the approximation errors. The higher order sliding surface can enhance the performance of controllers. The distances between cells are considered in order to further realize multi-cell cooperative control. In addition, the stability and convergence of the proposed NSOFCMNN are proved by the Lyapunov function, and the learning law is updated online by the gradient descent method. The simulation results show that the control system based on the proposed NSOFCMNN can effectively complete the cell manipulation task of optical tweezers and has better control performance than other neural network controllers.

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Section: Cell Dynamics Modelmentioning

confidence: 99%

“…In the study of multicellular cooperative control, by extending Equation (33), the dynamics of the ith trapped cell is expressed as:…”

Section: Cell Manipulation Control Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network

et al. 2022

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“…The CMAC is a adaptive neural network of form inquires type to express complex nonlinear function. The network can change algorithm of content by learning, and it has the ability to store information classification [15]. The basic idea of CMAC is: In the input space, a state is given.…”

Section: Design Of Based On the Cmac With Fuzzy-immune-pid Compound C...mentioning

confidence: 99%

The Resistance-Heated Furnace Temperature Control Based on CMAC-Fuzzy Immune PID Control

Wen

2010

AMM

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The control demand can not be satified by general PID algorithm because of time-change, nonlinear, uncertainty of controlled parameters in control process of the resistance-heated furnace. Based on regulative fuction of bilogic immune feedback response and characteristic of overhang nonlinear fuction with fuzzy discursion, and combining the advantages of CMAC(Cerebellar Model Articulation Controller), the CMAC-Fuzzy Immune-PID control strategy is applied to temperature control system of the resistance-heated furnace. Simulation study results show that control systems based on this method can attain high quality, is capable of adapting itself to variations of the control object’s parameters, and is featured by strong robustness and self-adaptability.

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“…However, the CMAC requires a large amount of memory for solving the problem of the high dimension, 37,38 is ineffective for online learning systems, 39 and has relatively poor function approximation ability. 40,41 Another problem is that it is difficult to determine the memory structure, e.g., to adaptively select structural parameters, in the CMAC model. 42,43 Recently, several researchers have proposed various solutions for the above problems, including fuzzy membership functions, 44 selection of learning parameters, 45 topology structure, 46 spline functions, 47 and fuzzy C-means.…”

Section: Introductionmentioning

confidence: 99%

Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

2016

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This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.

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Self-organizing CMAC neural networks and adaptive dynamic control

Cited by 21 publications

References 9 publications

Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network

Design of Optical Tweezers Manipulation Control System Based on Novel Self-Organizing Fuzzy Cerebellar Model Neural Network

The Resistance-Heated Furnace Temperature Control Based on CMAC-Fuzzy Immune PID Control

Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

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