Một mô hình suy diễn mờ phức không gian mới cho bài toán

Lê Trường Giang,Triệu Thu Hương,Nguyễn Long Giang,Lê Hoàng Sơn,Trần Mạnh Tuấn,Lương Thị Hồng Lan

semanticscholar（2021）

Cited 0|Views6

Abstract

Spatial and temporal changing detection in dataset of remote sensing images is very important to support in detect effectively the changing of natural events such as weather, climate, flood,... In this paper, we propose a novel spatial complex fuzzy inference system denoted as Spatial CFIS to support in detecting changes in remote sensing images. Our proposed model generates complex fuzzy rules using fuzzy clustering and provides predicted images using triangular spatial complex fuzzy rule base. To increase the performance, Spatial CFIS uses ADAM algorithm in order to optimize the weight set of rule base. The proposed model is experimented on a dataset from weather image data warehouse of USA Navy and evaluated by comparing with other models related to changing detection of sensing images. The implemental results show that Spatial CFIS is the best model in term of the value of RMSE.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
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Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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