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Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization

https://doi.org/10.3390/app13169379
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mop.23369openalex-snapshot
Particle swarm optimization application to microwave circuits
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Optimizing the Gain and Directivity of a Microstrip Antenna with Metamaterial Structures by Using Artificial Neural Network Approach
inferredcites →https://doi.org/10.1049/cje.2020.00.060openalex-snapshot
A Fast Two-Tone Active Load-Pull Algorithm for Assessing the Non-linearity of RF Devices
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EM-ANN models for microstrip vias and interconnects in dataset circuits
inferredcites →https://doi.org/10.1109/22.643868openalex-snapshot
Design and optimization of CPW circuits using EM-ANN models for CPW components
inferredcites →https://doi.org/10.1109/22.989962openalex-snapshot
Design of microwave filters
inferredcites →https://doi.org/10.1109/22.989963openalex-snapshot
Microwave filters-applications and technology
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/75.856986openalex-snapshot
A practical large-signal global modeling simulation of a microwave amplifier using artificial neural network
inferredcites →https://doi.org/10.1109/aces-china56081.2022.10065243openalex-snapshot
Machine Learning-Assisted Automatic Filter Synthesis with Prior Knowledge and Its Application to Single-Mode Bandpass Filter Design
inferredcites →https://doi.org/10.1109/apace.2010.5719750openalex-snapshot
Miniaturized microwave modified Koch fractal Hairpin Filter with harmonic suppression
inferredcites →https://doi.org/10.1109/cbs.2018.8612197openalex-snapshot
Continuous Reinforcement Learning With Knowledge-Inspired Reward Shaping for Autonomous Cavity Filter Tuning
inferredcites →https://doi.org/10.1109/iceei.2011.6021675openalex-snapshot
Multi objective optimization of a LNA using genetic algorithm based on NSGA-II
inferredcites →https://doi.org/10.1109/icmmt45702.2019.8992460openalex-snapshot
Multiobjective Optimization of Microwave Circuits with Many Structural Parameters and Objectives
inferredcites →https://doi.org/10.1109/issse.2007.4294399openalex-snapshot
Innovative Combline RF/Microwave Filter EM Synthesis and Design Using Neural Networks
inferredcites →https://doi.org/10.1109/jsen.2021.3124294openalex-snapshot
Optimal Design of Planar Microwave Microfluidic Sensors Based on Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/lapc.2015.7366036openalex-snapshot
Design and integration of a high-order hairpin bandpass filter with a spurious suppression circuit
inferredcites →https://doi.org/10.1109/lawp.2015.2514065openalex-snapshot
Transmitarray Antenna Design Using Forward and Inverse Neural Network Modeling
inferredcites →https://doi.org/10.1109/lmwc.2011.2127465openalex-snapshot
Simple and Efficient High-Dimensional Parametric Modeling for Microwave Cavity Filters Using Modular Neural Network
inferredcites →https://doi.org/10.1109/lmwc.2019.2952763openalex-snapshot
Attention-Based Deep Neural Network Behavioral Model for Wideband Wireless Power Amplifiers
inferredcites →https://doi.org/10.1109/lmwc.2020.2986156openalex-snapshot
Inverse Modeling for Filters Using a Regularized Deep Neural Network Approach
inferredcites →https://doi.org/10.1109/lmwc.2021.3101258openalex-snapshot
Efficient EM Optimization Exploiting Parallel Local Sampling Strategy and Bayesian Optimization for Microwave Applications
inferredcites →https://doi.org/10.1109/nemo.2015.7415085openalex-snapshot
High frequency HEMT modeling using artificial neural network technique
inferredcites →https://doi.org/10.1109/rfit52905.2021.9565305openalex-snapshot
An Overview of Neural Network Techniques for Microwave Inverse Modeling
inferredcites →https://doi.org/10.1109/robio.2015.7419091openalex-snapshot
Reinforcement learning approach to learning human experience in tuning cavity filters
inferredcites →https://doi.org/10.1109/tmtt.2003.820898openalex-snapshot
Computer-Aided Optimization of Nonlinear Microwave Circuits With the Aid of Electromagnetic Simulation
inferredcites →https://doi.org/10.1109/tmtt.2005.862709openalex-snapshot
Compact microstrip bandpass filters with good selectivity and stopband rejection
inferredcites →https://doi.org/10.1109/tmtt.2008.919078openalex-snapshot
Neural Network Inverse Modeling and Applications to Microwave Filter Design
inferredcites →https://doi.org/10.1109/tmtt.2009.2036412openalex-snapshot
High-Dimensional Neural-Network Technique and Applications to Microwave Filter Modeling
inferredcites →https://doi.org/10.1109/tmtt.2018.2841889openalex-snapshot
Multivalued Neural Network Inverse Modeling and Applications to Microwave Filters
inferredcites →https://doi.org/10.1109/tmtt.2019.2963639openalex-snapshot
Homotopy Optimization of Microwave and Millimeter-Wave Filters Based on Neural Network Model
inferredcites →https://doi.org/10.23919/ropaces.2018.8364310openalex-snapshot
Design and analysis of a bandpass hairpin filter
inferredcites →https://doi.org/10.3390/app13095322openalex-snapshot
Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
inferredcites →https://doi.org/10.3390/electronics8121506openalex-snapshot
Open-Source Software for Electromagnetic Scattering Simulation: The Case of Antenna Design
inferredcites →https://doi.org/10.3390/math11030598openalex-snapshot
A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter
inferredcites →https://doi.org/10.3390/mi13111810openalex-snapshot
Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator
inferredcites →https://doi.org/10.3390/sym15061272openalex-snapshot
From Netlist to Manufacturable Layout: An Auto-Layout Algorithm Optimized for Radio Frequency Integrated Circuits
inferredcites →https://doi.org/10.33969/jiec.2021.31001openalex-snapshot
Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4686134openalex-extracted
inferred← citeshttps://doi.org/10.1016/j.sna.2024.115433openalex-snapshot
Harnessing artificial neural networks for inverse design and analysis of MEMS-based Fabry–Pérot filters
inferred← citeshttps://doi.org/10.1088/1402-4896/aded42openalex-snapshot
Design and optimization of a bandpass filter using machine learning
inferred← citeshttps://doi.org/10.1088/1742-6596/2809/1/012015openalex-snapshot
Computer-aided design of MEMS-FP based on reinforcement learning
inferred← citeshttps://doi.org/10.1109/iws61525.2024.10713516openalex-snapshot
Deep Reinforcement Learning for Efficient Design and Optimization of Complementary Split Ring Resonator Filters
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