دانلود رایگان مقاله انگلیسی بهبود عملکرد یادگیری عمیق با استفاده از الگوریتم یادگیری بیرونی HTM جنگل تصادفی - IEEE 2018

Abstract

Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction.The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecastprocedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.

CONCLUSION

Deep learning is an important field in machine learning using Artificial Intelligence. The using of these systems depends on its behavior and performance. The main objective of this paper is to improve the performance of this type using a combined version from Random Forest and HTM Cortical Learning Algorithm. The proposed algorithm is called RFHTMC. The results depict that the proposed algorithm can reduce the mean absolute percentage error by half. And increases the overlap duty cycle by 15%.The proposed set of rules can increase the overall performance of the deep gaining knowledge of system by means of the preceding values. And it within the identical time can maintain the values of active duty cycle. Also, it maintains the mixed integer representation of input data units and the durability of the system.