Robust fusion algorithm based on RBF neural network with TS fuzzy model and its application to infrared flame detection problem

Wen, Ziteng; Xie, Lihua; Feng, Hao; Yong, Tan

doi:10.1016/j.asoc.2018.12.019

Cited by 30 publications

(15 citation statements)

References 22 publications

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“…Although fusion precision is improved, the method can only aggregate accurate data and cannot process complete data. Ziteng Wen et al [25] proposed a robust data fusion algorithm for data distortion, data loss, and signal saturation during infrared flame detection. The algorithm combines Radial Basis Function (RBF) neural network and Takagi Sugeno (TS) fuzzy model, and the experimental data collected by the three-channel infrared flame detector is used to verify the robustness of the proposed method.…”

Section: Related Workmentioning

confidence: 99%

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Sun¹,

Zhang²,

Zheng³

et al. 2019

Sensors

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Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

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Section: Related Workmentioning

confidence: 99%

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Sun¹,

Zhang²,

Zheng³

et al. 2019

Sensors

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“…Recently researchers have successfully blended RBFNN with other established techniques as well. For example [28,44,45], Yang et al in [45] proposed an efficient method for the selection of the centers using the conventional K-means clustering. However, unnecessary points around cluster centers were removed during global K-means clustering using population density method.…”

Section: Introductionmentioning

confidence: 99%

Multi-Kernel Fusion for RBF Neural Networks

Atif

Khan

Naseem

et al. 2022

Neural Process Lett

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A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches.

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“…Poro-roGAN (Zeng et al, 2019) aims to improve the semantic relevance and overall quality of the images via a variety of textual alignment modules and a patch-based image discriminator. also improve upon the StoryGAN architecture by upgrading the story encoder, GRU network, and discriminators and adding Weighted Activation Degree (Wen et al, 2019). Song et al (2020) is a more recent work which makes improvements to the Sto-ryGAN architecture; the primary contribution is adding a figure-ground generator and discriminator, which segments the figures and the background of the image.…”

Section: Introductionmentioning

confidence: 99%

Improving Generation and Evaluation of Visual Stories via Semantic Consistency

Maharana¹,

Hannan²,

Bansal³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Story visualization is an underexplored task that falls at the intersection of many important research directions in both computer vision and natural language processing. In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the captions. Prior work has introduced recurrent generative models which outperform text-to-image synthesis models on this task. However, there is room for improvement of generated images in terms of visual quality, coherence and relevance. We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentiallyconsistent story visualization, and (3) MARTbased transformers to model complex interactions between frames. We present ablation studies to demonstrate the effect of each of these techniques on the generative power of the model for both individual images as well as the entire narrative. Furthermore, due to the complexity and generative nature of the task, standard evaluation metrics do not accurately reflect performance. Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images. We also present correlation experiments of our proposed automated metrics with human evaluations. 1

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Robust fusion algorithm based on RBF neural network with TS fuzzy model and its application to infrared flame detection problem

Cited by 30 publications

References 22 publications

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

Multi-Kernel Fusion for RBF Neural Networks

Improving Generation and Evaluation of Visual Stories via Semantic Consistency

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