Evolving artificial neural networks with feedback

Herzog, Sebastian; Tetzlaff, Christian; Wörgötter, Florentin

doi:10.1016/j.neunet.2019.12.004

Cited by 65 publications

(42 citation statements)

References 23 publications

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“…The population consisting of 15 individuals was accepted. The individuals in the current generation are estimated using the following fitness function [47]:…”

Section: Ga Settingsmentioning

confidence: 99%

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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020

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Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

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“…The population consisting of 15 individuals was accepted. The individuals in the current generation are estimated using the following fitness function [47]:…”

Section: Ga Settingsmentioning

confidence: 99%

“…During the research, the GA was stopped when the maximum number of 50 generations was reached and/or when no change was detected in the best value of the fitness function for 10 maximum stall generations. A more detailed description of the GA used in DLNN is presented in [47].…”

Section: Ga Settingsmentioning

confidence: 99%

The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020

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“…Compared to the artificial neural network, the support vector machine has strong adaptive and generalization capability for high-dimensional and nonlinear data [32]. It can convert assessment problems into convex optimization problems with equal value, such that the weight of indicators can be determined according to the data distribution [33]. It overcomes the problem of local overfitting, but also suffers from problems such as difficult sample partitioning, subjective parameter selection, and overlearning [34].…”

Section: Introductionmentioning

confidence: 99%

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Wang

Yong

et al. 2021

Agriculture

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Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

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“…Artificial neural networks, which can find various input and output mapping functions via learning and training and by adjusting the threshold and weight values, are used to simulate biological neural networks and process information [27]. Any continuous function can establish a mapping relationship with a three-layer neural network [28].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance

Liu

Guang-ming

et al. 2021

Metals

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The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points.

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Evolving artificial neural networks with feedback

Cited by 65 publications

References 23 publications

The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Multi-Objective Optimization Design of Corrugated Steel Sandwich Panel for Impact Resistance

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