检索结果-内蒙古大学图书馆

Annual Reviews in Control 1997年 21卷 93-101页

作者： Horacek, Petr Binder, Zdenek Czech Technical University of Prague Faculty of Electrical Engineering Dept. of Control Engineering CZ-121 35 Prague 2 Karlovo nam.13 Czech Republic Inst. Natl. Polytech. de Grenoble ENSIEG Lab. d'Automatique de Grenoble 38402 Saint-Martin-d'Hères B.P.46 France

The paper presents Fuzzy Logic Controllers (FLC) with hierarchically structured rule base as nonlinear blocks used in a control system for direct control or supervision of standard controllers. Unified approach based on Fuzzy Logic Neural Networks (FLNN) is used to reveal the essential functions of a FLC. The FLNN is then extended to the case where decomposition of a rule base leads to simplification of controller design and tuning. Two structures of hierarchically organized groups of rules are discussed. Single-stage decision making structure with groups of rules organized hierarchically according to their specificity and a multi-stage decision making architecture with chained rules. Data induced error backpropagation technique for fine tuning of parameters of the second structure is derived.

关键词： backpropagation algorithms Fuzzy control Fuzzy inputs Hierarchical control Neural networks

来源：评论

学校读者我要写书评

暂无评论

An orthogonal neural network for function approximation

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1996年第5期26卷 779-785页

作者： Yang, SS Tseng, CS Department of Mechanical Engineering National Central University Chungli Taiwan

This paper presents a new single-layer neural network which is based on orthogonal functions. This neural network is developed to avoid the problems of traditional feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements, The desired output accuracy determines the required number of processing elements, Because weights are unique, the training of the neural network converges rapidly, An experiment in approximating typical continuous and discrete functions is given, The results show that the neural network has excellent performance in convergence time and approximation error.

关键词： Neural networks Function approximation Feedforward neural networks Convergence Multi-layer neural network Polynomials backpropagation algorithms Approximation error Control system synthesis Control systems

来源：评论

学校读者我要写书评

暂无评论

DATA-COMPRESSION OF THE ECG USING NEURAL NETWORK FOR DIGITAL HOLTER MONITOR

引用

IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1990年第3期9卷 53-57页

作者： IWATA, A NAGASAKA, Y SUZUMURA, N Department of Electrical and Computer Engineering Nagoya Institute of Technology Japan

A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.< >

关键词： Data compression Electrocardiography Neural networks Monitoring Artificial neural networks Heart beat Electrodes Thorax Digital signal processors backpropagation algorithms

来源：评论

学校读者我要写书评

暂无评论

ADAPTIVE NEAREST NEIGHBOR PATTERN-CLASSIFICATION

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 1991年第2期2卷 318-322页

作者： GEVA, S SITTE, J Faculty of Information Technology Queensland University of Technology Brisbane QLD Australia

We describe a variant of nearest neighbor pattern classification (NN) [1] and supervised learning by learning vector quantization (LVQ) [2], [3]. The decision surface mapping method, which we call DSM, is a fast supervised learning algorithm, and is a member of the LVQ family of algorithms. A relatively small number of prototypes are selected from a training set of correctly classified samples. The training set is then used to adapt these prototypes to map the decision surface separating the classes. This algorithm is compared with NN pattern classification, learning vector quantization (LVQ1) [2], and a two-layer perception trained by error backpropagation [4]. When the class boundaries are sharply defined (i.e., no classification error in the training set) the DSM algorithm outperforms these methods with respect to error rates, learning rates, and the number of prototypes required to describe class boundaries.

关键词： Nearest neighbor searches Pattern classification Prototypes Neural networks Books Computer networks Supervised learning backpropagation algorithms Vector quantization Error analysis

来源：评论

学校读者我要写书评

暂无评论

MULTILAYER FEEDFORWARD NEURAL NETWORKS WITH SINGLE POWERS-OF-2 WEIGHTS

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1993年第8期41卷 2724-2727页

作者： TANG, CZ KWAN, HK Department of Electrical Engineering University of Windsor Windsor ONT Canada

A new algorithm for designing multilayer feedforward neural networks with single powers-of-two weights is presented in this correspondence. By applying this algorithm, the digital hardware implementation of such networks becomes easier as a result of the elimination of multipliers. This proposed algorithm consists of two stages. First, the network is trained by using the standard backpropagation algorithm. Weights are then quantized to single powers-of-two values, and weights and slopes of activation functions are adjusted adaptively to reduce sum of squared output errors to a specified level. Simulation results indicate that the multilayer feedforward neural networks with single powers-of-two weights obtained using the proposed algorithm have similar generalization performance as the original networks with continuous weights.

关键词： Multi-layer neural network Neural networks Feedforward neural networks backpropagation algorithms Neurons Quantization Algorithm design and analysis Neural network hardware Artificial neural networks Computer networks

来源：评论

学校读者我要写书评

暂无评论

DIAGNOSING DYNAMIC FAULTS USING MODULAR NEURAL NETS

引用

IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS 1993年第2期8卷 44-53页

作者： LEONARD, JA KRAMER, MA MIT DEPT CHEM ENGNCAMBRIDGEMA 02139

The use of radial basis function networks (RBFNs) for diagnosis and classification is discussed. Even though RBFNs can be trained quickly compared to backpropagation networks, the training effort is still significant for large-scale diagnosis problems. Rho-Net, an architecture that decomposes the dynamic classification problem in two ways, making such training tractable, is presented. The first decomposition reduces the amount of training data needed for any stage of the training process by constructing separate networks for each fault class. The second decomposition reduces the dimensionality of the input space by incorporating temporal information at the output of the network, instead of as a temporal window at the input of the net. Application of Rho-Nets to chemical process simulation is discussed.

关键词： Neural networks Testing Radial basis function networks Gaussian processes Extrapolation backpropagation algorithms Organizing Feature extraction Supervised learning Large-scale systems

来源：评论

学校读者我要写书评

暂无评论

Smart Proxy Modeling of a Fractured Reservoir Model for Production Optimization: Implementation of Metaheuristic Algorithm and Probabilistic Application

引用

NATURAL RESOURCES RESEARCH 2021年第3期30卷 2431-2462页

作者： Ng, Cuthbert Shang Wui Jahanbani Ghahfarokhi, Ashkan Nait Amar, Menad Torsaeter, Ole Norwegian Univ Sci & Technol Dept Geosci & Petr Trondheim Norway Dept Etud Thermodynam Div Labs Sonatrach Boumerdes Algeria

Numerical reservoir simulation has been recognized as one of the most frequently used aids in reservoir management. Despite having high calculability performance, it presents an acute shortcoming, namely the long computational time induced by the complexities of reservoir models. This situation applies aptly in the modeling of fractured reservoirs because these reservoirs are strongly heterogeneous. Therefore, the domains of artificial intelligence and machine learning (ML) were used to alleviate this computational challenge by creating a new class of reservoir modeling, namely smart proxy modeling (SPM). SPM is a ML approach that requires a spatio-temporal database extracted from the numerical simulation to be built. In this study, we demonstrate the procedures of SPM based on a synthetic fractured reservoir model, which is a representation of dual-porosity dual-permeability model. The applied ML technique for SPM is artificial neural network. We then present the application of the smart proxies in production optimization to illustrate its practicality. Apart from applying the backpropagation algorithms, we implemented particle swarm optimization (PSO), which is one of the metaheuristic algorithms, to build the SPM. We also propose an additional procedure in SPM by integrating the probabilistic application to examine the overall performance of the smart proxies. In this work, we inferred that the PSO had a higher chance to improve the reliability of smart proxies with excellent training results and predictive performance compared with the considered backpropagation approaches.

关键词： Reservoir simulation Dual-porosity dual-permeability Smart proxy modeling backpropagation algorithms Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Neural network models for a resource allocation problem

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第2期28卷 276-284页

作者： Walczak, S Univ S Alabama Sch Comp & Informat Sci Mobile AL 36688 USA

University admissions and business personnel offices use a limited number of resources to process an ever-increasing quality of student and employment applications. Application systems are further constrained to identify and acquire, in a limited time period,those candidates who are most likely to accept an offer of enrollment or employment. Neural networks are a blew methodology;to this particular domain. Various neural network architectures and learning algorithms are analyzed comparatively to determine the applicability of supervised learning neural networks to the domain problem of personnel resource allocation and to identify optimal learning strategies in this domain. This paper Focuses on multilayer perceptron backpropagation, radial basis function, counterpropagation, general regression, fuzzy ARTMAP, and linear vector quantization neural networks. Each neural network predicts the probability of enrollment and nonenrollment for individual student applicants. backpropagation networks produced the best overall performance. Network performance results are measured by the reduction in the counselor's student case load and corresponding increases in student enrollment. The backpropagation neural networks achieve a 56% reduction in counselor case load.

关键词： Neural networks Resource management Personnel Employment backpropagation algorithms Algorithm design and analysis Supervised learning Multilayer perceptrons Fuzzy neural networks Vector quantization

来源：评论

学校读者我要写书评

暂无评论

Training partially recurrent neural networks using evolutionary strategies

引用

IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1997年第2期5卷 192-194页

作者： Greenwood, GW Dept. of Electr. & Comput. Sci. Western Michigan Univ. Kalamazoo MI USA

This correspondence presents the latest results of using evolutionary strategies (ES's) to design partially recurrent neural networks for viseme recognition. ES's are stochastic optimization algorithms based upon the principles of natural selection found in the biological world. Our results indicate that ES's can be effectively used to determine the synaptic weights in neural networks and can outperform backpropagation techniques.

关键词： Recurrent neural networks Speech recognition Lips Pipeline processing Telephony Auditory system Stochastic processes backpropagation algorithms Neural networks

来源：评论

学校读者我要写书评

暂无评论

Computational capabilities of local-feedback recurrent networks acting as finite-state machines

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第6期7卷 1521-1525页

作者： Frasconi, P Gori, M UNIV SIENA DIPARTIMENTO INGN I-53100 SIENA ITALY

In this paper we explore the expressive power of recurrent networks with local feedback connections for symbolic data streams, We rely on the analysis of the maximal set of strings that can be shattered by the concept class associated to these networks (i.e., strings that can be arbitrarily classified as positive or negative), and find that their expressive power is inherently limited, since there are sets of strings that cannot be shattered, regardless of the number of hidden units. Although the analysis holds for networks with hard threshold units, we claim that the incremental computational capabilities gained when using sigmoidal units are severely paid in terms of robustness of the corresponding representation.

关键词： Computer networks Computer architecture Robustness backpropagation algorithms Artificial neural networks Multilayer perceptrons Recurrent neural networks Turing machines Output feedback Neurofeedback

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：