Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

Al-Batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Zamli, Kamal Z.; Azizli, Khairun Azizi Mohd

doi:10.1016/j.asoc.2009.06.018

Cited by 44 publications

(13 citation statements)

References 26 publications

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“…Both controller parameters were estimated using RLS as an estimation algorithm in order to reduce the cost function. RLS algorithm has been used extensively in adaptive filtering, self-tuning control, system identification and prediction, and interference cancellation [8]. For the neural network applications, Azimi-Sadjadi and Liou [9] introduced the RLS algorithm with constant forgetting factor as the training algorithm for the MLP network.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Sharun

Mashor

Norhayati

et al. 2010

2010 International Conference on Computer Applications and Industrial Electronics

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The performance comparison between two controllers, namely Adaptive Neuro-Controller (ANC), based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box Controller (APBBC) are presented in this paper. The comparison is based on the capability of the controlled output tracking the model reference output and the percentage of overshoot. Both controllers are based on a black box approach that offers simpler design approach. The Model Reference Adaptive System (MRAS) has been used to generate the desired output path and to ensure the output of the controlled system follows the output of the reference model. Recursive Least Square (WRLS) algorithm will be used to adjust the controller parameters to minimize the error between the plant output and the model reference output. The controllers have been tested using a linear plant and a nonlinear plant with several varying operating conditions. The simulation results show that output response of ANC have slightly better tracking performance compared to APBB controller for linear plant and have equivalent performance for nonlinear plant.

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Section: Introductionmentioning

confidence: 99%

Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Sharun

Mashor

Norhayati

et al. 2010

2010 International Conference on Computer Applications and Industrial Electronics

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“…The above-mentioned models of the clarification process focused on pH prediction, while more critical process parameters such as the gravity purity have not been involved. Moreover, these models are mainly based on the gradient descent method to update the model parameters (Al-Batah, Mat Isa, Zamli, & Azizli, 2010). While the generalization performance of these models is good, there are some problems, such as slow training speed and easy to fall into the local optimum, which limits the application and development of the model (Kaya & Uyar, 2013;Mohammed, Minhas, Jonathan Wu, & Sid-Ahmed, 2011).…”

Section: Introductionmentioning

confidence: 99%

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Meng

Wang

et al. 2019

Food Science & Nutrition

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Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.

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“…The RLS algorithm has been used extensively in adaptive filtering, self-tuning control, system identification and prediction, and interference cancellation [20]. In neural network applications, Azimi-Sadjadi and Liou [21] had introduced the RLS algorithm with constant forgetting factor as training algorithm for MLP network.…”

Section: Estimation Algorithmmentioning

confidence: 99%

Adaptive Neuro-Controller Based on Hybrid Multi Layered Perceptron Network for Dynamic Systems

Sharun¹,

Mashor²,

Jaafar³

et al. 2012

CONTROL

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In this paper, an intelligent controller namely Adaptive Neuro-controller (ANC) based on Hybrid Multi Layered Perceptron (HMLP) network has been developed for dynamic systems. The performance of ANC has been compared with the ANC based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box (APBB) Controller. The comparison are based on the time response and the capability of the controlled output to track the model reference output. All controllers are based on a black box approach that offers simpler design approach. The Model Reference Adaptive System (MRAS) has been used to generate the desired output path and to ensure the output of the controlled system follows the output of the reference model. Weighted Recursive Least Square (WRLS) algorithm has been used to adjust the controller parameters in order to minimize the error between the plant output and the model reference output. The controllers have been tested using a linear plant and a nonlinear plant with several varying operating conditions such as varying gain, noise and disturbance. Based on the simulation results and performance analysis for all controllers, it is observed that ANC based on HMLP network is controllable and more stable than ANC based on MLP network and APBB controller. It is also can be signify that the ANC based on HMLP network is sufficient to control the plants with unpredictable conditions.

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Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

Cited by 44 publications

References 26 publications

Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Adaptive Neuro-Controller Based on Hybrid Multi Layered Perceptron Network for Dynamic Systems

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