163 skills found · Page 5 of 6
yhtang123 / Intelligent High Efficiency Energy Conversion SystemAbstract- As yet, the efficiency optimization of the power electronic converters needs to rely on its circuit model, while an inaccurate model cannot represent the correct operation behavior of the converter. Therefore, the accuracy of the power electronic converter model is of great importance for the efficiency optimization. However, the existing modeling methods cannot provide accurately model for the power electronic converters, since the parasitic parameters of its structure are closely related to the components and their layout, and the device structure size. Moreover, due to the power electronic converters usually contain many switching devices and their operating conditions are very complicated in practical application, thus many variable parameters need to be taken into account in the efficiency optimization process, which aggravates the computational complexity of optimization procedure for the efficiency. Based on the analysis mentioned above, the existing methods cannot provide the optimal efficiency optimization modulation strategy for the power electronic converters. Although, the artificial intelligence (AI) is powerful for solving the optimization and decision-making problems of difficult modeling and high-dimensional complex systems, its applications in the power electronic converters efficiency optimization are still being developed. Inspired by the successful application of the robotic chemist, and the classic games, here, we present an AI aided efficiency optimization engineer for the first time, which can train online to search for improved the operation efficiency for the dual active bridge (DAB) converter without prior knowledge about their circuit model. The engineer operated autonomously around the clock in a practical circuit platform about 71 hours, performing 120, 000 consecutive experiments within a six-variable experimental space, driven by the deep deterministic policy gradient (DDPG) algorithm. This autonomous exploring approach found an optimized modulation strategy, which can greatly improve the efficiency under entire continuous operation range compares to existing methods, especially under light load conditions. This online optimization approach can be deployed in the conventional power electronic converters for a range of operation performance optimization problems beyond the DAB converter. Our study created a novel idea and expanded the frontier theory for the power electronics automatic optimization.
charlescao2019 / Capstone Stock Trading Policy Optimization MasterStock investment can be one of the ways to manage one’s asset. Technical analysis is sometimes used in financial markets to assist traders make buying and selling decisions [1]. Many technical analysis trading rules are deterministic trading policies. [2] uses genetic algorithm to find technical trading rule. [3] studies evolutionary algorithms in optimization of technical rules for automated stock trading. [4] proposed a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. [5] studies the selection of the optimal trading model for stock investment in different industries. [6] describes the optimization of trading strategies. The optimization of trading rule using genetic algorithm or evolutionary algorithms belongs to policy-based method, which is a branch of Machine Learning. Policy-based methods try to directly optimize for the optimal policy which is an important branch for domains with continuous action spaces [7]. There are studies focus on how to find a trading strategy via Reinforcement Learning (RL) [8] or using Deep-Q learning for automatic trading algorithm [9]. But in this study we will focus on the policy-based method using Generic Algorithm that directly search for the optimal parameters of a deterministic policy. Yahoo Finance’s stock history data [10] will be used in this study. The reason to choose Yahoo Finance data is because it is free and available for public to assess. The performance of algorithm will be evaluated using different stocks. The purpose of the study is to see the difference between using an agent with optimized policy to manage one’s asset with buy-and-hold strategy, or manage one’s asset with an agent with unoptimized policy.
harshit2997 / Standard And Chaotic Firefly AlgorithmMATLAB implementations of standard and chaos-incorporated versions of the firefly metaheuristic tested on continuous optimization functions.
rkruegs123 / Idp DesignOptimizing IDPs directly at the level of simulations via a continuous sequence representation.
UW-ACL / Ct ScvxSuccessive Convexification for Trajectory Optimization with Continuous-Time Constraint Satisfaction
gianluigigrandesso / CactoContinuous Actor Critic algorithm with Trajectory Optimization
NiranjanBhujel / Pendulum PPOImplementation of Proximal Policy Optimization (PPO) for continuous action space (`Pendulum-v1` from gym) using tensorflow2.x and pytorch.
harshit2997 / Standard And Chaotic Particle Swarm OptimizationMATLAB implementations of standard and chaos-incorporated versions of the particle swarm optimization metaheuristic tested on continuous optimization functions.
ignavierng / Notears AdmmTowards Federated Bayesian Network Structure Learning with Continuous Optimization
DavisWu01 / QQLMPAThis is the code of QQLMPA, which is proposed in <A quasi-opposition learning and Q-learning based marine predators algorithm for global continuous optimization problems>
xtyang16 / River2River2: A Tightly-coupled Multi-Radar Multi-IMU Velocity Estimator Based on Continuous-time Optimization
1QB-Information-Technologies / CcvmSolve continuous non-convex optimization problems with Coherent Continous-Variable Machine (CCVM) architectures and solvers
Mattral / MATLAB Optimization AlgorithmThe firefly algorithm (FA) is a nature-inspired metaheuristic optimization algorithm developed by Xin-She Yang that is inspired by the flashing behavior of fireflies (Yang, 2008), originally designed to solve continuous optimization problems.
zhaolin820 / Beamforming Optimization For Continuous Aperture Array Based CommunicationsThe code for the paper "Beamforming Optimization for Continuous Aperture Array (CAPA)-based Communications" in IEEE Transactions on Wireless Communications
Valdecy / Metaheuristic Ant Lion OptimizerAnt Lion Optimizer to Minimize Functions with Continuous Variables
footoredo / Rspo Iclr 2022The official code repository for "Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization".
JuliaML / ContinuousOptimization.jlA playground for implementations of unconstrained continuous full-batch optimization algorithms
Valdecy / Metaheuristic Artificial Bee Colony OptimizationArtificial Bee Colony Optimization to Minimize Functions with Continuous Variables
abolfazlshirazi / EDAPPEDA++: Estimation of Distribution Algorithms with Feasibility Conserving Mechanisms for Constrained Continuous Optimization
ChengHust / IEEE CEC 2023 CompetitionThe Platform for CEC 2023 Competition on "Large-scale Continuous Optimization for Non-contact Measurement"
