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3rd Information Systems International Conference

作者： Sholahuddin, Asep Ramadhan, Agung Pre Supriatna, Asep K. Padjadjaran State Univ Fac Math & Nat Sci Dept Math Informat Engn Study Program Jatinangor 45363 Indonesia Padjadjaran State Univ Fac Math & Nat Sci Dept Math Math Study Program Jalan Raya Bandung Sumedang Km 21 Jatinangor 45363 Indonesia

Overfishing is a global environmental problem that risks fisheries since many of the fish stock of the fisheries have already reduced to below a tolerable level. One of solutions that often implemented in the fishery management is by calculating the value of Maximum Sustainable Yield (MSY) as the maximum tolerable harvest that can be taken out from the natural stock without harming the population over an indefinite period of time. A proper tool used for computing the MSY is needed to support the fishery manager in solving this decision making problem. In this paper we propose a software development of Decision Support System (DSS) to address such fishery industry problem. The DSS is developed to compute the MSY from the annual yield-effort data of the fishery. We use two sigmoid growth equations, Logistic and Gompertz equations, as the underlying population models, which then are approximated by their discrete forms for computing several growth parameters. Most known methods of growth parameter estimation use a Multiple linear Regression with Ordinary Least Square method (MLR-OLS). Here we propose the application of Artificial Neural Network with linear perceptron method (ANN-LP). A case study in this paper shows that the effectiveness of the proposed ANN-LP is as good as the MLR-OLS in estimating both the growth parameters and the MSY of the fishery in the case study. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： DSS MSY Logistic Gompertz ANN linear perceptron

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The Application of ANN-linear perceptron in the Development of DSS for a Fishery Industry

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Procedia Computer Science 2015年 72卷 67-77页

作者： Asep Sholahuddin Agung Pre Ramadhan Asep K. Supriatna Informatics Engineering Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Jalan Raya Bandung Sumedang Km. 21 Jatinangor-45363 Indonesia Mathematics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Jalan Raya Bandung Sumedang Km. 21 Jatinangor-45363 Indonesia

关键词： DSS MSY Logistic Gompertz ANN linear perceptron

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Multimodal Neural Network for Recognition of Cardiac Arrhythmias Based on 12-Load Electrocardiogram Signals

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IEEE ACCESS 2023年 11卷 133744-133754页

作者： Kiladze, Mariya R. Lyakhova, Ulyana A. Lyakhov, Pavel A. Nagornov, Nikolay N. Vahabi, Mohsen North Caucasus Fed Univ Dept Math Modeling Stavropol 355017 Russia North Caucasus Fed Univ North Caucasus Ctr Math Res Stavropol 355017 Russia Shahrood Univ Technol Fac Elect Engn Shahrud *** Iran

Automatic classification of heart rhythm disturbances using an electrocardiogram is a reliable way to timely detect diseases of the cardiovascular system. The need to automate this process is to increase the number of electrocardiogram signals. Classification methods based on the use of neural networks provide a high percentage of arrhythmia recognition. However, known classification methods do not take into account patient characteristics. The work proposes a multimodal neural network that takes into account the age and gender characteristics of the patient. It includes a Long short-term memory (LSTM) network for feature extraction on twelve-channel electrocardiogram signals and a linear neural network for processing patient metadata such as age and gender. Extraction of electrocardiogram signal features occurs in parallel with metadata processing. The last unifying layer of the proposed multimodal neural network integrates heterogeneous data and features of electrocardiogram signals obtained using an LSTM network. The developed multimodal neural network was verified using the PhysioNet/Computing in Cardiology Challenge 2021 ECG database. The simulation results showed that the proposed multimodal neural network achieves a recognition accuracy of 63%, which is 2 percentage points higher compared to state-of-the-art methods.

关键词： Neural network classification metadata linear perceptron LSTM network PhysioNet/Computing in Cardiology Challenge 2021

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Neural Network Based Real-time Correction of Transducer Dynamic Errors

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MEASUREMENT SCIENCE REVIEW 2013年第6期13卷 286-291页

作者： Roj, J. Silesian Tech Univ Inst Measurement Sci Elect & Control Gliwice Poland

In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.

关键词： Artificial neural network linear perceptron dynamic errors correction measuring transducer state variables

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Node perturbation learning without noiseless baseline

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NEURAL NETWORKS 2011年第3期24卷 267-272页

作者： Cho, Tatsuya Katahira, Kentaro Okanoya, Kazuo Okada, Masato Univ Tokyo Grad Sch Frontier Sci Chiba 2778561 Japan Riken Brain Sci Inst Wako Saitama 3510198 Japan Japan Sci & Technol Agcy ERATO Okanoya Emot Informat Project Wako Saitama 3510198 Japan

Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient by comparing an evaluation of the perturbed output and the unperturbed output performance, which we call the baseline. Node perturbation learning has primarily been investigated without taking noise on the baseline into consideration. In real biological systems, however, neural activities are intrinsically noisy, and hence, the baseline is likely contaminated with the noise. In this paper, we propose an alternative learning method that does not require such a noiseless baseline. Our method uses a "second perturbation", which is calculated with different noise than the first perturbation. By comparing the evaluation of the outcomes with the first perturbation and with the second perturbation, the network weights are updated. We reveal that the learning speed showed only a linear decrease with the variance of the second perturbation. Moreover, using the second perturbation can lead to a decrease in residual error compared to the case of using the noiseless baseline. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Node perturbation Noiseless baseline Stochastic gradient method linear perceptron Learning curve

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Optimal node perturbation in linear perceptrons with uncertain eligibility trace

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NEURAL NETWORKS 2010年第2期23卷 219-225页

作者： Katahira, Kentaro Cho, Tatsuya Okanoya, Kazuo Okada, Masato Univ Tokyo Grad Sch Frontier Sci Chiba 2778561 Japan RIKEN Brain Sci Inst Wako Saitama 3510198 Japan ERATO Okanoya Emot Informat Project Japan Sci & Technol Agcy Wako Saitama 3510198 Japan

Node perturbation learning has been receiving Much attention as a method for achieving stochastic gradient descent. As it does not require direct gradient calculations, it can be applied to a reinforcement learning framework. However, in conventional node perturbation learning, the residual error due to perturbation is not eliminated even after convergence. Using infinitesimal perturbations suppresses the residual error, but such perturbations are less robust against uncertainty and noise in an eligibility trace, which is a memory of perturbation and input. We derive an optimal parameter schedule for node perturbation learning used with linear perceptrons with uncertainty in the eligibility trace. Our adaptive learning rule resolves the trade-off between robustness against the uncertainty and residual error reduction. The results obtained will be useful in designing learning rules and interpreting related biological knowledge. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Node perturbation Uncertainty linear perceptron Learning curve Residual error

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A theoretical analysis of on-line learning using correlated examples

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第9期E91A卷 2663-2670页

作者： Seki, Chihiro Sakurai, Shingo Matsuno, Masafumi Miyoshi, Seiji Kobe City Coll Technol Adv Course Elect & Elect Engn Kobe Hyogo 6512194 Japan Matsushita Excel Technol Co Ltd Osaka 5300001 Japan Sysmex Corp Kobe Hyogo 6510073 Japan Kobe City Coll Technol Dept Elect Engn Kobe Hyogo 6512194 Japan Fujitsu FSAS Inc Tokyo 1050011 Japan Kansai Univ Fac Engn Sci Dept Elect & Elect Engn Suita Osaka 5648680 Japan

In this paper we analytically investigate the generalization performance of learning using correlated inputs in the framework of on-line learning with a statistical mechanical method. We consider a model composed of linear perceptrons with Gaussian noise. First, we analyze the case of the gradient method. We analytically clarify that the larger the correlation among inputs is or the larger the number of inputs is, the stricter the condition the learning rate should satisfy is, and the slower the learning speed is. Second, we treat the block orthogonal projection learning as an alternative learning rule and derive the theory. In a noiseless case, the learning speed does not depend on the correlation and is proportional to the number of inputs used in an update. The learning speed is identical to that of the gradient method with uncorrelated inputs. On the other hand, when there is noise, the larger the correlation among inputs is, the slower the learning speed is and the larger the residual generalization error is.

关键词： linear perceptron on-line learning statistical mechanical method generalization error gradient method block orthogonal projection learning

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Ensemble of linear perceptrons with confidence level output

Ensemble of linear perceptrons with confidence level output

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4th International Conference on Hybrid Intelligent Systems (HIS 04)

作者： Hartono, P Hashimoto, S Waseda Univ WABOT HOUSE Lab Shinjuku Ku Tokyo 1698555 Japan

ISBN: (纸本)0769522912

In this study we introduce an ensemble of neural networks, in which each member is a linear perceptron. Our main objective is to build an ensemble of neural networks that can automatically and effectively divide the problem space and assign a subspace to each member By assigning only a portion of the problem space, we expect that the learning difficulty for each member can be reduced, thus leading to better classification ability. To investigate the effectiveness of the proposed method in dividing the problem space, in this paper we deal with ensemble consists only of linear perceptrons, each with an additional output neuron that indicates the confidence level of the output.

关键词： neural network ensemble linear perceptron confidence level competitive learning problem subspace

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