检索结果-内蒙古大学图书馆

learning algorithmS FOR NEURAL NETWORKS AND NEURO-FUZZY SYSTEMS WITH SEPARABLE STRUCTURES

CYBERNETICS AND SYSTEMS ANALYSIS 2015年第2期51卷 173-186页

作者： Skorohod, B. A. Sevastopol Natl Tech Univ Sevastopol Ukraine

This article considers the problem of training neural networks and neuro-fuzzy systems, which lead to separable models, i.e., structures nonlinear with respect to some unknown parameters and linear with respect to others. New algorithms for training them are proposed that are based on a nonlinear optimization problem including a priori information only on nonlinear input parameters. It is assumed that this information can be obtained from a training set, the distribution of a generating set, or linguistic information. To solve the problem, the Gauss-Newton method with linearization in the vicinity of the last estimate, asymptotic representations of the pseudo-inverse of perturbed matrices, and separable structures of models are used. The obtained algorithms have the following important properties: they do not require the selection of initial values of linearly entering parameters, which can lead to divergence, but, at the same time, it is not necessary to find partial derivatives of a projection matrix;they can be used in serial and batch processing;well-known algorithms are obtained from them as special cases, and a simulation has shown that the proposed algorithms can outperform the former in accuracy and convergence rate.

关键词： separable regression neural network neuro-fuzzy system learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform

引用

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2025年 12卷 1528224页

作者： Spannenkrebs, Jan Benedict Eiermann, Aron Zoll, Thomas Hackenschmidt, Silke Kabisch, Johannes NTNU Inst Biotechnol & Food Sci Trondheim Norway Proteineer GmbH Neu Isenburg Germany Tech Univ Darmstadt Comp Aided Synthet Biol Darmstadt Germany

One goal of synthetic biology is to provide well-characterised biological parts that behave predictably in genetic assemblies. To achieve this, each part must be characterised in a time-resolved manner under relevant conditions. Robotic platforms can be used to automate this task and provide sufficiently large and reproducible data sets including provenance. Although robotics can significantly speed up the data collection process, the collation and analysis of the resulting data, needed to reprogram and refine workflows for future iterations, is often a manual process. As a result, even in times of rapidly advancing artificial intelligence, the common design-build-test-learn (DBTL) cycle is still not circular without human intervention. To move towards fully automated DBTL cycles, we developed a software framework to enable a robotic platform to autonomously adjust test parameters. This interdisciplinary work between computer science and biology thus transforms a static robotic platform into a dynamic one. The software framework includes software components such as an importer that retrieves measurement data from the platform's devices and writes it to a database. This is followed by an optimizer that selects the next measurement points based on a balance between exploration and exploitation. The platform is shown to be able to automatically and autonomously optimize the inducer concentration for a Bacillus subtilis system and the combination of inducer and feed release for a Escherichia coli system. As a target product the readily measurable green fluorescent reporter protein (GFP) is produced over multiple, consecutive iterations of testing. An evaluation of chosen (learning) algorithms for single and dual factor optimization was performed. In this article, we share the lessons learned from the development, implementation and execution of this automated design-build-test-learn cycles on a robotic platform.

关键词： automation synthetic biology design-build-test-learn (DBTL) cycle autonomous system learning algorithm

来源：评论

学校读者我要写书评

暂无评论

learning algorithms for quality of service multicast routing

引用

ELECTRONICS LETTERS 1998年第12期34卷 1195-1197页

作者： Reeve, J Mars, P Hodgkinson, T Univ Durham Sch Engn Ctr Telecommun Networks Durham DH1 3LE England BT Labs Ipswich IP5 7RE Suffolk England

Stochastic learning automata are used to 'grow' quality of service bounded multicast trees in a dynamic membership environment. It is found that learning automata, which use minimal state information and require only local connectivity knowledge, provide superior results to alternative shortest path approaches by learning to minimise the number of blocked connection attempts via load balancing.

关键词： dynamic membership telecommunication network routing tree shortest path telecommunication services quality of service multicast routing stochastic automata load balancing trees (mathematics) stochastic learning automata local connectivity telecommunication channels learning algorithm learning automata blocking probability

来源：评论

学校读者我要写书评

暂无评论

RANFIS-Based Sensor System with Low-Cost Multi-Sensors for Reliable Measurement of VOCs

引用

TECHNOLOGIES 2025年第3期13卷 111-111页

作者： Kim, Keunyoung Yang, Woosung Kwangwoon Univ Dept Robot 20 Kwangwoon Ro Seoul 01897 South Korea

This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based senso

关键词： multi-sensor system ANFIS RANFIS learning algorithm low-cost sensing system VOCs

来源：评论

学校读者我要写书评

暂无评论

learning algorithmS OF FORM STRUCTURE FOR BAYESIAN NETWORKS

LEARNING ALGORITHMS OF FORM STRUCTURE FOR BAYESIAN NETWORKS

引用

IEEE International Conference on Image Processing

作者： Philippot, Emilie Belaid, Yolande Belaid, Abdel Campus Sci BP 239 F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9781424479948

In this paper, a new method is presented for the recognition of online forms filled manually by a digital-type clip. This writing process is not very restrictive but it is only sending electronic ink without the pre-printed form, which will require to undertake field recognition without context. To identify the form model of filled fields, we propose a method based on Bayesian networks. The neworks use the conditional probabilities between fields in order to infer the real structure. We associate multiple Bayesian networks for different structures levels (i.e. sub-structures) and test different algorithms for for structure learning. The experiments were conducted on the basis of 3200 forms provided by the Actimage compagny, specialist in interactive writing processes. The first results show a recognition rate reaching more than 97%.

关键词： Bayes methods belief networks conditional probability digital-type clip document image processing electronic ink field recognition form structure interactive writing processes learning algorithm multiple Bayesian network online form recognition preprinted form structure learning

来源：评论

学校读者我要写书评

暂无评论

An extended TLD tracking algorithm using co-training learning for low frame rate videos

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 24743-24769页

作者： Moridvaisi, Hooman Razzazi, Farbod Pourmina, Mohammadali Dousti, Massoud Islamic Azad Univ Dept Elect & Comp Engn Sci & Res Branch Tehran Iran

The conventional tracking-learning-detection (TLD) algorithm is sensitive to illumination changes, clutter, significant changes of target shape between consecutive frames. In addition, low frame rate scenarios result in drift in object position or even missing the object. To solve these problems and enhance the tracking robustness, in this paper, TLD algorithm was extended in two folds. First, Kalman filter and Mean-shift algorithms were combined for the tracking part and second, co-training semi-supervised learning algorithm was used for the learning part of the conventional TLD structure. The Kalman filter estimates the position of the target in the next frame based on the previous positions of the target. This reduces tracking failure. On the other hands, the Mean-shift tracking algorithm is robust to rotation, partial occlusion and scale changing. In the learning part of TLD structure, two training tracking algorithms with two independent classifiers were run on the current frame simultaneously. Its structure makes data of both pools (color features and target templates) update by the results of other algorithm, in addition to the results of the corresponding algorithm in each of the tracking and detection algorithms. Therefore, classifiers can learn faster changing features of the target during the consecutive frames in online tracking process. Finally, the extended structure can solve the problem of lost object in LFR videos tracking and other similar challenges simultaneously. In terms of overlap ratio metric, comparing with conventional TLD and extended kernelized correlation filters (EKCF) algorithms, the success rate of our algorithm under various scenarios has increased by 161.03% and 255.82% respectively and under other scenarios, in terms of precision metric, it has increased by 18.46% and 1479.47%, respectively. Accordingly, comparative evaluations of the proposed method to other top state-of-the-art tracking algorithms under various scenarios present

关键词： Object tracking Kalman filter Mean-shift algorithm learning algorithm Tracking learning detection

来源：评论

学校读者我要写书评

暂无评论

learning using distance based training algorithm for pattern recognition

引用

PATTERN RECOGNITION LETTERS 2004年第2期25卷 189-196页

作者： Seow, MJ Asari, VK Old Dominion Univ Dept Elect & Comp Engn VLSI Syst Lab Norfolk VA 23529 USA

A modular approach for improving the performance of 2-D Hopfield neural network is presented in this paper. The approach is inspired by biological visual perception phenomenon. The training method introduces a decaying distance factor into the Hebbian learning rule for image processing applications. The value of the distance factor varies based on the spatial location of the neurons with respect to the neuron under consideration. Experiments performed with character images show that the new approach can learn and recognize patterns very effectively. (C) 2003 Elsevier B.V. All rights reserved.

关键词： learning algorithm pattern recognition hopfield network distance based training algorithm

来源：评论

学校读者我要写书评

暂无评论

Convergence Analysis of a Class of Hyvarinen-Oja's ICA learning algorithms With Constant learning Rates

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第5期57卷 1811-1824页

作者： Lv, Jian Cheng Tan, Kok Kiong Yi, Zhang Huang, Sunan Sichuan Univ Machine Intelligence Lab Coll Comp Sci Chengdu 610054 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

The convergence of a class of Hyvarinen-Oja's independent component analysis (ICA) learning algorithms with constant learning rates is investigated by analyzing the original stochastic discrete time(SDT) algorithms and the corresponding deterministic discrete time (DDT) algorithms. Most existing learning rates for ICA learning algorithms are required to approach zero as the learning step increases. However, this is not a reasonable requirement to impose in many practical applications. Constant learning rates overcome the shortcoming. On the other hand, the original algorithms, described by the SDT algorithms, are studied directly. Invariant sets of these algorithms are obtained so that the nondivergence of the algorithms is guaranteed in stochastic environment. In the invariant sets, the local convergence of the original algorithms is analyzed by indirectly studying the convergence of the corresponding DDT algorithms. It is rigorously proven that the trajectories of the DDT algorithms starting from the invariant sets will converge to an independent component direction with a positive kurtosis or a negative kurtosis. The convergence results can shed some light on the dynamical behaviors of the original SDT algorithms. Furthermore, the corresponding DDT algorithms are extended to the block versions of the original SDT algorithms. The block algorithms not only establish a relationship between the SDT algorithms and the corresponding DDT algorithms, but also can get a good convergence speed and accuracy in practice. Simulation examples are carried out to illustrate the theory derived.

关键词： Deterministic discrete time (DDT) algorithm independent component analysis (ICA) invariant set learning algorithm stochastic discrete time (SDT) algorithm

来源：评论

学校读者我要写书评

暂无评论

learning-Based Anti-Hebbian algorithm for Control of Distribution Static Compensator

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2014年第11期61卷 6004-6012页

作者： Arya, Sabha Raj Singh, Bhim Chandra, Ambrish Al-Haddad, Kamal Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada

This paper deals with an implementation of a distribution static compensator (DSTATCOM) using a learning-based anti-Hebbian control algorithm for compensation of linear/nonlinear loads. The proposed control algorithm is used for extraction of fundamental active and reactive power components of load currents for estimating reference supply currents. This control algorithm is implemented on a developed DSTATCOM using a digital signal processor for reactive power compensation, harmonics elimination, and load balancing. Simulation and test results demonstrate satisfactory performance of the proposed control algorithm for the control of DSTATCOM under varying loads.

关键词： Distribution static compensator (DSTATCOM) harmonics learning algorithm load balancing power factor correction (PFC) reactive power compensation zero voltage regulation (ZVR)

来源：评论

学校读者我要写书评

暂无评论

Development and evaluation of adaptive metacognitive scaffolding for algorithm-learning system

引用

IET SOFTWARE 2019年第4期13卷 305-312页

作者： Hidayah, Indriana Adji, Teguh Bharata Setiawan, Noor Akhmad Univ Gadjah Mada Fac Engn Dept Elect Engn Jl Grafika 2 Yogyakarta Indonesia

Adaptive metacognitive scaffolding is developed to provide learning assistance on an as-needed basis;thus, advances the effectiveness of computer-based learning systems. Metacognitive scaffoldings have been developed for some science subjects;however, not for algorithm-learning. The learning algorithm is different from learning science as it is more oriented to problem-solving;therefore, this study is aimed to describe the modelling, development, and evaluation of the adaptive metacognitive scaffolding which is dedicated for encouraging algorithm-learning. In addition, the authors present a new approach for learner modelling to find students' metacognitive state. Adaptivity of the scaffolding is based on the learner modelling. To evaluate the effectiveness of the developed system, it is deployed in a real algorithm-learning classroom of 38 students. The class is randomly divided into two groups: experiment and control. Two parameters are measured from both groups, i.e. academic success and academic satisfaction. Non-parametric statistical test, i.e. Mann-Whitney U-test (significance level 0.01) rejects the null hypothesis (U-value = 86.5 and U-critical = 101). This result verifies that the academic success of the experiment group is significantly higher than that of the control group. In addition, an academic satisfaction survey shows that adaptive scaffolding is valid in assisting students while learning with the system.

关键词： computer aided instruction statistical testing adaptive metacognitive scaffolding learning assistance learning algorithm learner modelling algorithm-learning classroom algorithm-learning system computer-based learning systems academic success academic satisfaction nonparametric statistical test Mann-Whitney U-test

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：