DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions

Hryniowski, Andrew; Wong, Alexander

doi:10.48550/arxiv.1911.09257

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Figure 4 illustrates the layout of such a DNN with fully connected layers. State-ofthe-art DNNs incorporate various other types of layers, including RBFN [178], SVR [179], and TSK fuzzy [180,181] layers. DNNs can be endowed with the ability to handle time series data by incorporating long short-term memory (LSTM) or gated recurrent unit (GRU) layers [176,182].…”

Section: Deep Neural Networkmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

et al. 2023

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Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research.

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Section: Deep Neural Networkmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

et al. 2023

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“…4 illustrates the layout of such a DNN with fully connected layers. State-of-the-art DNNs incorporate various other types of layers, including RBFN [175], SVR [176], and TSK fuzzy [177,178] layers. DNNs can be endowed with the ability to handle time series data by incorporating of long short-term memory (LSTM) or gated recurrent unit (GRU) layers [173,179].…”

Section: Deep Neural Networkmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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“…Activation functions with curvature are especially common in ANNs with only a couple of layers. For example, activation functions in radial basis function (RBF) networks [936][937][938][939] , which are efficient universal approximators, are often Gaussians, multiquadratics, inverse multiquadratics, or square-based RBFs 940 .…”

Section: Nonlinear Activationmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions

Cited by 4 publications

References 21 publications

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Review: Deep Learning in Electron Microscopy

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