인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. One of the main challenges in this process lies in the nonlinear relationship between screw positions and the filter’s frequency response, making conventional tuning methods difficult and inefficient. Additionally, while intelligent algorithms can assist in tuning, they often require large volumes of simulated data, leading to high computational costs. However, reducing the dataset size can compromise accuracy, as important frequency response information may be lost. To overcome these limitations, PCA is applied to minimize the dimensionality of the S11 response data, keeping only the most relevant information while improving computational efficiency. A feedforward neural network is employed to predict the PCA-reduced S-parameters, serving as a surrogate model that enables faster decision-making within the Q-learning framework. By integrating PCA at the data preprocessing stage, the number of frequency points is reduced from 401 to 20, significantly accelerating the Q-learning convergence process. The proposed approach, successfully reduces the tuning process from 1000 steps to just 45, ensuring faster and more precise optimization.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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