Conjugate Gradient Back-propagation with Modified Polack –Rebier updates for training feed forward neural network

Al-Bayati, Abbas Y.; Saleh, Ibrahem A.; Abbo, Khalil K.

doi:10.33899/iqjoss.2011.27897

Cited by 3 publications

(2 citation statements)

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“…2.5. Conjugate gradient backpropagation with Polak-Ribiére updates (CGP) [37] The search direction in the Fletcher-Reeves algorithm is determined using equation (7…”

Section: Gradient Descent With Momentum Backpropagation (Traingdm) [34]mentioning

confidence: 99%

Performance analysis of various training algorithms of deep learning based controller

2023

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Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10−14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.

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“…2.5. Conjugate gradient backpropagation with Polak-Ribiére updates (CGP) [37] The search direction in the Fletcher-Reeves algorithm is determined using equation (7…”

Section: Gradient Descent With Momentum Backpropagation (Traingdm) [34]mentioning

confidence: 99%

Performance analysis of various training algorithms of deep learning based controller

2023

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“…The evolution of NNs in control systems spans decades, progressing from single-layer networks in the 1950s to multilayer networks with the advent of the back-propagation algorithm in 1986. Overcoming challenges associated with deep NNs took another two decades, culminating in the development of DL, addressing issues like computational load and vanishing gradient [8,[21][22][23]. Control engineering has witnessed a surge in research on model-free controllers (MFCs), particularly data-driven controllers, over the last two decades.…”

Section: Introductionmentioning

confidence: 99%

Analysis of DC motor for process control application using neural network predictive controller

Prasad,

Kumar,

Singh

2024

Eng. Res. Express

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A DC motor is a critical actuator in process control systems. This study investigates the effectiveness of a deep learning (DL) based Neural Network Predictive Controller (NNPC) for precise DC motor speed control. The NNPC anticipates the motor’s future behaviour based on its current state and control inputs. The controller then optimally generates inputs to minimise tracking errors and enhance system performance. The NNPC demonstrated a remarkable reduction in Mean Squared Error (MSE), achieving a training MSE of 2.75 × 10−14 and the best validation MSE of 9.2023 × 10−14. These quantitative outcomes affirm the reliability and robustness of the proposed NNPC for speed control in DC motor systems across diverse applications.

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Comparison of Neural Network Training Algorithms for Classification of Heart Diseases

Karim

Niakan

Safdari

2018

IJ-AI

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<span lang="EN-US">Heart disease is the first cause of death in different countries. Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart disease. There are different training algorithms for ANN. We compared eight neural network training algorithms for classification of heart disease data from UCI repository containing 303 samples. Performance measures of each algorithm containing the speed of training, the number of epochs, accuracy, and mean square error (MSE) were obtained and analyzed. Our results showed that training time for gradient descent algorithms was longer than other training algorithms (8-10 seconds). In contrast, Quasi-Newton algorithms were faster than others (<=0 second). MSE for all algorithms was between 0.117 and 0.228. While there was a significant association between training algorithms and training time (p<0.05), the number of neurons in hidden layer had not any significant effect on the MSE and/or accuracy of the models (p>0.05). Based on our findings, for development an ANN classification model for heart diseases, it is best to use Quasi-Newton training algorithms because of the best speed and accuracy.</span>

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Conjugate Gradient Back-propagation with Modified Polack –Rebier updates for training feed forward neural network

Cited by 3 publications

References 0 publications

Performance analysis of various training algorithms of deep learning based controller

Performance analysis of various training algorithms of deep learning based controller

Analysis of DC motor for process control application using neural network predictive controller

Comparison of Neural Network Training Algorithms for Classification of Heart Diseases

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