A radial sampling-based subregion partition method for dendrite network-based reliability analysis

Lu, Li; Wu, Yongge; Zhang, Qi; Qiao, Ping; Xing, Tao

doi:10.1080/0305215x.2022.2137876

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Moreover, the appliances energy consumption is predicted and analyzed by Gang neurons to improve energy efficiency and cut carbon dioxide emissions. Study using Gang neurons [49]:"A radial sampling-based subregion partition method for dendrite network-based reliability analysis"(see Fig. 32)…”

Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

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Artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence in recent years. The neuron of ANNs was designed by the stereotypical knowledge of biological neurons 70 years ago. Artificial Neuron is expressed as f(wx+b) or f(WX). This design does not consider dendrites’ information processing capacity. However, some recent studies show that biological dendrites participate in the pre-calculation of input data. Concretely, biological dendrites play a role in extracting the interaction information among inputs (features). Therefore, it may be time to improve the neuron of ANNs. In this study, some dendritic modules with excellent properties are proposed and added to artificial neurons to form new neurons named Gang neurons. E.g., The dendrite function can be expressed as Wi,i-1Ai-1 ○ A0|1|2|...|i-1 . The generalized new neuron can be expressed as f(W(Wi,i-1Ai-1 ○ A0|1|2|...|i-1)).The simplified new neuron be expressed as f(∑(WA ○ X)). After improving the neurons, many networks can be tried. This paper shows some basic architecture for reference in the future. Up to now, others and the author have applied Gang neurons to various fields, and Gang neurons show excellent performance in the corresponding fields. Interesting things: (1) The computational complexity of dendrite modules (Wi,i-1Ai-1 ○ Ai-1) connected in series is far lower than Horner's method. Will this speed up the calculation of basic functions in computers? (2) The range of sight of animals has a gradient, but the convolution layer does not have this characteristic. This paper proposes receptive fields with a gradient. (3) The networks using Gang neurons can delete Fully-connected Layer. In other words, the parameters in Fully-connected Layers are assigned to a single neuron, which reduces the parameters of a network for the same mapping capacity. (4) ResDD(ResDD modules+One Linear module) can replace the current ANNs’ Neurons. ResDD has controllable precision for better generalization capability. Gang neuron code is available at https://github.com/liugang1234567/Gang-neuron.

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Section: A Polynomial Convolution and Dendrite In This Papermentioning

confidence: 99%

It may be time to improve the neuron of artificial neural network

Liu¹

2023

Preprint

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“…Literature [24] An Algorithm for Precipitation Correction in Flood Season Based on Dendritic Neural Network…”

Section: Applications Literaturementioning

confidence: 99%

Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

et al. 2022

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Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.

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A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization

Zhang

et al. 2023

Mathematics

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In order to overcome the drawbacks of expensive function evaluation in the practical reliability-based design optimization (RBDO) problem, researchers have proposed the black box-based RBDO method. The algorithm flow of the commonly employed RBDO method for the black box problem consists of the outer construction loop of the surrogate model of the constraint function and the inner surrogate model-based solving loop. To improve the solving ability of the black box RBDO problem, this paper proposes a transformation-based improved kriging method to increase the effectiveness of the two loops identified above. For the outer loop, a sample distribution-based learning function is suggested to improve the construction efficiency of the surrogate model of the constraint function. For the inner loop, a paired incremental sample-based limit reliability boundary construction approach is suggested to transform the RBDO problem into an equivalent deterministic design optimization problem that can be efficiently solved by classical optimization algorithms. The test results of five cases demonstrate that the proposed method can accurately construct the surrogate model of the constraint function and efficiently solve the black box RBDO problem.

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A radial sampling-based subregion partition method for dendrite network-based reliability analysis

Cited by 3 publications

References 38 publications

It may be time to improve the neuron of artificial neural network

It may be time to improve the neuron of artificial neural network

Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization

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