An identifier-actor-optimizer policy learning architecture for optimal control of continuous-time nonlinear systems

Cheng, Lin; Wang, Zhen‐Bo

doi:10.1007/s11433-019-1481-2

Cited by 2 publications

(1 citation statement)

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“…It implies on functions a differentiable sphere. AdaSwarm includes an exponentially weighted momentum flyspeck swarm optimizer (EMPSO) for making effective analysis [9]. e authors discovered an ATMO (AdapTive Meta Optimizers) which integrates two different optimizers for importing the benefactions and produces the result with a single optimizer [10].…”

Section: Related Workmentioning

confidence: 99%

Bio-Optimization of Deep Learning Network Architectures

Shanmugavadivu

Rani

Chitra

et al. 2022

Security and Communication Networks

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Deep learning is reaching new heights as a result of its cutting-edge performance in a variety of fields, including computer vision, natural language processing, time series analysis, and healthcare. Deep learning is implemented using batch and stochastic gradient descent methods, as well as a few optimizers; however, this led to subpar model performance. However, there is now a lot of effort being done to improve deep learning’s performance using gradient optimization methods. The suggested work analyses convolutional neural networks (CNN) and deep neural networks (DNN) using several cutting-edge optimizers to enhance the performance of architectures. This work uses specific optimizers (SGD, RMSprop, Adam, Adadelta, etc.) to enhance the performance of designs using different types of datasets for result matching. A thorough report on the optimizers’ performance across a variety of architectures and datasets finishes the study effort. This research will be helpful to researchers in developing their framework and appropriate architecture optimizers. The proposed work involves eight new optimizers using four CNN and DNN architectures. The experimental results exploit breakthrough results for improving the efficiency of CNN and DNN architectures using various datasets.

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Section: Related Workmentioning

confidence: 99%