ارائه الگوریتم یادگیری انتقالی برای بهبود سرعت و صحت همگرایی در اتوماتای یادگیر سلولی
محورهای موضوعی : مهندسی برق و کامپیوتر
سید امیرهادی مینوفام
1
(دانشگاه آزاد اسلامی، واحد قزوین)
اعظم باستان فرد
2
(دانشگاه آزاد اسلامی، واحد کرج)
محمدرضا کیوان‌پور
3
(دانشگاه الزهرا)
کلید واژه: اتوماتای یادگیر سلولی, انتقال دانش, نرخ همگرایی, یادگیری انتقالی,
چکیده مقاله :
: اتوماتای یادگیر سلولی، یک مدل هوشمند به صورت آمیزه‌ای از اتوماتای سلولی و اتوماتای یادگیر است. پایین‌بودن سرعت همگرایی در اتوماتای یادگیر سلولی یکی از چالش‌های اساسی به شمار می‌رود. در این مطالعه، الگوریتم گسترش‌یافته‌ای از اتوماتای یادگیر سلولی مبتنی بر یادگیری انتقالی به نام TL-CLA پیشنهاد می‌گردد که از یادگیری انتقالی به عنوان راهکاری برای کاهش محاسبات و کمینه‌سازی چرخه یادگیری بهره می‌‌گیرد. مدل گسترش‌یافته پیشنهادی بر اساس تابع شایستگی و بردار نگرش برای انتقال یادگیری طراحی شده است. در الگوریتم TL-CLA، ابتدا مقدار تابع شایستگی بر اساس محیط محلی و مقدار بردار نگرش بر مبنای محیط سراسری اتوماتا محاسبه می‌شود. زمانی که این دو معیار حد آستانه مقرر را کسب کنند، انتقال بردار احتمالات اقدام ها سبب انتقال یادگیری از اتوماتای یادگیر سلولی منبع به اتوماتای یادگیر سلولی مقصد می‌شود. نتایج آزمایش‌ها نشان می‌دهند که مدل پیشنهادی TL-CLA در محیط‌های عملیاتی استاندارد با دو اقدام و چند اقدام، به طور میانگین، به ترتیب به اندازه 7/2% و 2/2% از نظر صحت همگرایی افزایش یافته است. نرخ همگرایی نیز به طور میانگین، به ترتیب 8% و 2% بهبود داشته است. اتوماتای یادگیر سلولی TL-CLA پیشنهادی در انتقال دانش حاصل از یادگیری یک وظیفه برای وظیفه‌ای مشابه کاربرد دارد.
Cellular learning automaton is an intelligent model as a composition of cellular automaton and learning automaton. In this study, an extended algorithm of cellular learning automata is proposed based on transfer learning as the TL-CLA algorithm. In this algorithm, transfer learning is used as an approach for computation deduction and minimizing the learning cycle. The proposed algorithm is an extended model based on merit function and attitude vector for transfer learning. In the TL-CLA algorithm, the value of the merit function is computed based on the local environment, and the value of the attitude vector is calculated based on the global environment. When these two measures get the threshold values, the transfer of action probabilities causes the transfer learning from the source CLA to the destination CLA. The experimental results show that the proposed TL-CLA model leads to increment the convergence accuracy as 2.7% and 2.2% in two actions and multi-action standard environments, respectively. The improvements in convergence rate are also 8% and 2% in these two environments. The TL-CLA could be applied in knowledge transfer from learning one task to learning another similar task
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