Accelerated learning in MLP using adaptive learning rate with momentum coefficient

Sheel, Satya; Varshney, Tarun; Varshney, Ruchi

doi:10.1109/iciinfs.2007.4579193

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…A large value of the learning rate may skip the optimal solution and the convergence may DOI: 10.5281/zenodo.5188523 Received: January 12, 2021 Accepted: July 10, 2021 25 never be achieved. On the other hand, a small learning rate will increase the total time to converge to the optimal value or trap in local minima [20,21]. Therefore, selecting the optimal value of the learning rate is a major challenge regarding the convergence rate.…”

Section: Adaptive Learning Ratementioning

confidence: 99%

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Safara¹

2021

JISIoT

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A medical condition that causes disability and early neurological and cognitive condition is autism spectrum disorder (ASD). Gene expression and environment have an impact on this medical condition. Development of diagnostic instruments and skills improved the autism recognition and increased the society awareness about it. To cope with this disorder collaboration between families, service providers, and autistic individuals is a necessity. Early diagnosis of ASD could help in lessening stress, increase adaptation, and support welfare in healthcare systems. Therefore, a large body of research is attempting to provide an intelligent medical diagnostic system to identify and diagnose ASD in early stages using machine learning methods. In this paper, several multilayer perceptron neural network is proposed for ASD detection in healthcare systems. The learning rate is adaptively tuned to achieve the best results. The results show that the approach proposed in this study achieved 99.6% accuracy, which indicates the superiority of the proposed method in identifying and detecting autism disorder in comparison with similar previous methods.

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Section: Adaptive Learning Ratementioning

confidence: 99%

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Safara¹

2021

JISIoT

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“…Marwala (2007) fulfilled training of the Bayesian NN by using Markov chain-based Monte Carlo technique in genetic programming. Sheell, Varshney, and Varshney (2007) heuristically arranged learning coefficients depending on the sign of the error between the desired and output results. Kathirvalavakumar and Subavathi (2009) improved a modified backpropagation algorithm in neighbourhood-based NNs by replacing fixed LPs with adaptive LPs.…”

Section: Related Workmentioning

confidence: 99%

Design of highly effective multilayer feedforward neural network by using genetic algorithm

2020

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This paper presents a highly effective and precise neural network method for choosing the activation functions (AFs) and tuning the learning parameters (LPs) of a multilayer feedforward neural network by using a genetic algorithm (GA). The performance of the neural network mainly depends on the learning algorithms and the network structure. The backpropagation learning algorithm is used for tuning the network connection weights, and the LPs are obtained by the GA to provide both fast and reliable learning. Also, the AFs of each neuron in the network are automatically chosen by a GA. The present study consists of 10 different functions to accomplish a better convergence of the desired input–output mapping. Test studies are performed to solve a set of two‐dimensional regression problems for the proposed genetic‐based neural network (GNN) and conventional neural network having sigmoid AFs and constant learning parameters. The proposed GNN has also been tested by applying it to three real problems in the fields of environment, medicine, and economics. Obtained results prove that the proposed GNN is more effective and reliable when compared with the classical neural network structure.

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“…However, a known problem of the GD method is the learning rate selection, which affects the learning speed and stabilization. By appropriately choosing the learning rate, the stability of the controller can be guaranteed 11–14, the stability of the identification can be guaranteed 15–18, and the acceleration rate of the MLPNN learning can be increased 19, 20. In 21, the authors are suggested a stable and optimal learning rate, obtained by the genetic search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Extended fuzzy function model with stable learning methods for online system identification

Beyhan

Alcı

2010

Adaptive Control & Signal

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The aim of the online nonlinear system identification is the accurate modeling of the current local inputoutput behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real-time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete-time systems. The main contributions are to introduce an effective quasinonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient-descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input-to-state stability (ISS) approach. Numerical simulations are performed for Box-Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems.

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Accelerated learning in MLP using adaptive learning rate with momentum coefficient

Cited by 13 publications

References 7 publications

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Design of highly effective multilayer feedforward neural network by using genetic algorithm

Extended fuzzy function model with stable learning methods for online system identification

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