检索结果-内蒙古大学图书馆

Fuzzy neural network approaches for robotic gait synthesis

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2000年第4期30卷 594-601页

作者： Juang, JG Natl Taiwan Ocean Univ Inst Maritime Technol Keelung 20224 Taiwan

In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time." The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.

关键词： backpropagation through time fuzzy neural network gait synthesis membership function pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

Identifying preferred solutions for multi-objective optimization: application to capacitated vehicle routing problem

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2015年第4期18卷 1435-1448页

作者： Sooktip, Tipwimol Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Thonburi Dept Comp Engn 126 Pracha Uthit Rd Bangkok 10140 Thailand

In this paper, the preference-based methods are proposed to solve the multi-objective optimization of the capacitated vehicle routing problem (CVRP) considering transportation time on different types of road. The objective functions are to minimize the transportation distance and time subject to capacity constraint when the transportation time depends on types of road. We perform the multi-objective optimization for CVRP in three steps. In the first step, the customer nodes are clustered according to the geographical coordinates of the nodes. Therefore, each cluster can be handled separately and in parallel by using cloud computing which is effectively applied in various applications for a large data analysis. In the second step, non-dominated sorting genetic algorithm-II which is a well-known searching algorithm is applied to find the optimal routing paths from the depot to customers in each cluster. Finally, the preference-based method is considered to identify the appropriate solutions among the trade-off solutions (the non-dominated solutions) according to the decision maker's preferences. This method expresses the decision maker's preference as the reference values or ranking of the objective functions. The algorithm is demonstrated with a well-known instance of the CVRP.

关键词： Multi-objective optimization Capacitated vehicle routing problem pruning algorithm Preference-based method

来源：评论

学校读者我要写书评

暂无评论

Structural determination of multilayered large-signal neural-network HEMT model

引用

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 1998年第10期46卷 1367-1375页

作者： Shirakawa, K Shimizu, M Okubo, N Daido, Y Fujitsu Labs Ltd Wireless Commun Syst Lab Nakahara Ku Kawasaki Kanagawa 211 Japan Kanazawa Inst Technol Nonoichi Ishikawa 921 Japan

This paper reports on the structure of a large-signal neural-network (NN) high electron-mobility transistor (HEMT) model as determined by a pruning technique and a genetic algorithm, The bias-dependent intrinsic elements of an HEMT's equivalent circuit are described by a generalized multilayered NN whose inputs are the gate-to-source bias (V-gs) and the drain-to-source bias (V-ds). Using C-gs data as an example, we began by experimentally examining some of the features of the multilayered NN model to obtain rules-of-thumb on choosing training parameters and other information for succeeding studies. We then developed and studied a novel pruning technique to optimize the C-gs NN model. Excessively large NN configurations ran be reduced to an appropriate size by means of a weight decay, which is based on the analysis of a synaptic connection's activity. Finally, we employed a genetic algorithm for the same purpose, By representing the configuration of a standard multilayered NN as a chromosome, the optimum configuration of a C-gs model was obtained through a simulated evolution process, For this approach, the configuration of an NN that simultaneously represents seven intrinsic elements (C-gs, R-i, ..., C-da) of an equivalent circuit was also shown for comparison to previous work. Ne successfully obtained simplified NN models using both approaches. The advantages and disadvantages of these two approaches are discussed in the conclusion, To our knowledge, this is the first report to clarify the general process of building an NN device model.

关键词： genetic algorithm HEMT large-signal model neural network pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach to Enhance Scalability, Reliability and Computational Speed in LoRa Networks

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第9期11卷 463-469页

作者： Gopal, S. Raja Prabhakar, V. S. V. Koneru Lakshmaiah Educ Fdn Dept ECE Vaddeswaram Andhra Pradesh India

The spreading out of Internet of Things (IoT) facilitates with new wireless communication Technologies. To have reliable communication for long duration, low power wide area can be aimed at sensor nodes. These days, LoRa has become a recognizable preference for IoT based solutions. In this paper LoRa network performance is analyzed. A hybrid technique is proposed to overcome the scalability, reliability and computational speed issues in LoRa network. In the proposed hybrid technique, a lightweight scheduling technique is used to address scalability and reliability issues and then pruning algorithm is also incorporated to enhance the computational speed of LoRa network for IoT applications. Further, LoRa network for IoT applications is analyzed using LoRaWAN NS-3 module and the parameters packet error ratio (PER), network throughput and fairness for improved reliability and scalability are illustrated. Simulation results are obtained for multiple gateways scenarios. The analysis presents that the LoRa network has addressed scalability and reliability issues using lightweight scheduling technique and computational speed is also enhanced using pruning algorithm. Therefore, the hybrid technique illustrated for LoRa network for IoT applications is in good agreement.

关键词： Hybrid technique Internet of Things (IoT) lightweight scheduling LoRa pruning algorithm scalability reliability

来源：评论

学校读者我要写书评

暂无评论

RAMD: registry-based anomaly malware detection using one-class ensemble classifiers

引用

APPLIED INTELLIGENCE 2019年第7期49卷 2641-2658页

作者： Tajoddin, Asghar Abadi, Mahdi Tarbiat Modares Univ Sch Elect & Comp Engn Tehran Iran

Malware is continuously evolving and becoming more sophisticated to avoid detection. Traditionally, the Windows operating system has been the most popular target for malware writers because of its dominance in the market of desktop operating systems. However, despite a large volume of new Windows malware samples that are collected daily, there is relatively little research focusing on Windows malware. The Windows Registry, or simply the registry, is very heavily used by programs in Windows, making it a good source for detecting malicious behavior. In this paper, we present RAMD, a novel approach that uses an ensemble classifier consisting of multiple one-class classifiers to detect known and especially unknown malware abusing registry keys and values for malicious intent. RAMD builds a model of registry behavior of benign programs and then uses this model to detect malware by looking for anomalous registry accesses. In detail, it constructs an initial ensemble classifier by training multiple one-class classifiers and then applies a novel swarm intelligence pruning algorithm, called memetic firefly-based ensemble classifier pruning (MFECP), on the ensemble classifier to reduce its size by selecting only a subset of one-class classifiers that are highly accurate and have diversity in their outputs. To combine the outputs of one-class classifiers in the pruned ensemble classifier, RAMD uses a specific aggregation operator, called Fibonacci-based superincreasing ordered weighted averaging (FSOWA). The results of our experiments performed on a dataset of benign and malware samples show that RAMD can achieve about 98.52% detection rate, 2.19% false alarm rate, and 98.43% accuracy.

关键词： Windows malware Registry-based malware detection Ensemble classifier One-class classification pruning algorithm Memetic firefly algorithm Aggregation operator Superincreasing ordered weighted averaging

来源：评论

学校读者我要写书评

暂无评论

Design of a Prediction Model for Cement Rotary Kiln Using Wavelet Projection Fuzzy Inference System

引用

CYBERNETICS AND SYSTEMS 2012年第5期43卷 369-397页

作者： Sharifi, A. Shoorehdeli, M. Aliyari Teshnehlab, M. Islamic Azad Univ Dept Comp Sci & Res Branch Tehran Iran KNT Univ Technol Mechatron Dept Tehran Iran KNT Univ Technol Dept Elect Engn Tehran Iran

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.

关键词： cement rotary kiln gradient descent algorithm Morlet wavelet pruning algorithm TS-type fuzzy system

来源：评论

学校读者我要写书评

暂无评论

Estimating Correlated Rates of Trait Evolution with Uncertainty

引用

SYSTEMATIC BIOLOGY 2019年第3期68卷 412-429页

作者： Caetano, D. S. Harmon, L. J. Univ Idaho Inst Bioinformat & Evolutionary Studies IBEST Dept Biol Sci Moscow ID 83843 USA Univ Arkansas Dept Biol Sci Fayetteville AR 72701 USA

Correlated evolution among traits, which can happen due to genetic constraints, ontogeny, and selection, can have an important impact on the trajectory of phenotypic evolution. For example, shifts in the pattern of evolutionary integration may allow the exploration of novel regions of the morphospace by lineages. Here, we use phylogenetic trees to study the pace of evolution of several traits and their pattern of evolutionary correlation across clades and over time. We use regimes mapped to the branches of the phylogeny to test for shifts in evolutionary integration while incorporating the uncertainty related to trait evolution and ancestral regimes with joint estimation of all parameters of the model using Bayesian Markov chain Monte Carlo. We implemented the use of summary statistics to test for regime shifts based on a series of attributes of the model that can be directly relevant to biological hypotheses. In addition, we extend Felsenstein's pruning algorithm to the case of multivariate Brownian motion models with multiple rate regimes. We performed extensive simulations to explore the performance of the method under a series of scenarios. Finally, we provide two test cases;the evolution of a novel buccal morphology in fishes of the family Centrarchidae and a shift in the trajectory of evolution of traits during the radiation of anole lizards to and from the Caribbean islands.

关键词： Anolis Centrarchidae comparative methods evolutionary integration evolutionary rates modularity pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

SeNPIS: Sequential Network pruning by class-wise Importance Score

引用

APPLIED SOFT COMPUTING 2022年 129卷

作者： Pachon, Cesar G. Ballesteros, Dora M. Renza, Diego Univ Mil Nueva Granada Carrera 11 101-80 Bogota 110111 Colombia

In the last decade, pattern recognition and decision making from images has mainly focused on the development of deep learning architectures, with different types of networks such as sequential, residual and parallel. Although the depth and size varies between models, they all have in common that they can contain multiple filters or neurons that are not important for the purpose of prediction, and that do negatively impact the size of the model and their inference times. Therefore, it is advantageous to use pruning methods that, while largely maintaining the initial performance of the classifier, significantly reduce its size and FLOPs. In parameter reduction, the decision rule is generally based on mathematical criteria, e.g. the amplitude of the weights, but not on the actual impact of the filter or neuron on the classifier performance for each of the classes. Therefore, we propose SeNPIS as a method that involves both filter and neuron selection based on a class-wise importance score, and network resizing to increase parameter reduction and FLOPs in sequential CNN networks. Several tests were performed to compare SeNPIS with other representative state-of-the-art methods, for the CIFAR-10 and Scene-15 datasets. It was found that for similar values of accuracy, and even in some cases with a slight increase in accuracy, SeNPIS significantly reduces the number of parameters by up to an additional 23.5% (i.e., a 51.05% reduction with SeNPIS versus a 27.53% reduction with Gradient) and FLOPs by up to an additional 26.6% (i.e., a 74.82% reduction with SeNPIS versus a 48.16% reduction with Weight) compared to the Weight, Taylor, Gradient and LRP methods.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Deep learning Model compression pruning algorithm Importance score Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Neural Network for Pattern Classification

Fuzzy Neural Network for Pattern Classification

引用

International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Kulkarni, Arun Kulkarni, Nikita SGGS Inst Engn & Technol Nanded 413601 India Oracle Financial Serv Software Ltd Mumbai 400063 Maharashtra India

This research work proposes a fuzzy neural network (FNN) for pattern classification. The proposed network is the modified version of the Radial basis function neural network (RBFNN). FNN uses supervised fuzzy clustering and pruning algorithm to determine the precise number of clusters with proper centroid and width to form the processing nodes in the hidden layer. These clusters represent fuzzy set hyperspheres (FSHs), which are defined by the fuzzy membership function. The training between the hidden layer to output layer which is done by using the LMS algorithm in RBFNN is avoided, and the output is determined by using the fuzzy union operation. The fuzzy membership function shields the clustered patterns resulting in 100% accuracy for the data set used during training. Unlike other clustering algorithms used to construct the hidden layer of RBFNN, the proposed clustering algorithm is independent of tuning parameters and is fast in training and retrieval. Thus FNN reduces the computation time, guarantees 100% accuracy for any training set, and provides superior and comparable recognition accuracy for the datasets with the precise number of FSHs in the hidden layer. Hence the proposed FNN can be used for pattern classification. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Fuzzy neural network Radial basis function neural network pruning algorithm Fuzzy Membership function

来源：评论

学校读者我要写书评

暂无评论

Maximizing Hardware Prefetch Effectiveness with Machine Learning 17

Maximizing Hardware Prefetch Effectiveness with Machine Lear...

引用

2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC)

作者： Rahman, Saami Burtscher, Martin Zong, Ziliang Qasem, Apan Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9781479989379

Modern processors are equipped with multiple hardware prefetchers, each of which targets a distinct level in the memory hierarchy and employs a separate prefetching algorithm. However, different programs require different subsets of these prefetchers to maximize their performance. Turning on all available prefetchers rarely yields the best performance and, in some cases, prefetching even hurts performance. This paper studies the effect of hardware prefetching on multithreaded code and presents a machine-learning technique to predict the optimal combination of prefetchers for a given application. This technique is based on program characterization and utilizes hardware performance events in conjunction with a pruning algorithm to obtain a concise and expressive feature set. The resulting feature set is used in three different learning models. All necessary steps are implemented in a framework that reaches, on average, 96% of the best possible prefetcher speedup. The framework is built from open-source tools, making it easy to extend and port to other architectures.

关键词： learning (artificial intelligence) multi-threading performance evaluation public domain software storage management feature set hardware performance events hardware prefetch effectiveness maximization learning models machine learning memory hierarchy multiple hardware prefetchers multithreaded code open-source tools optimal prefetcher prediction performance maximization prefetcher speedup program characterization pruning algorithm algorithms Hardware Optimization Prefetching Testing Training prefetching memory hierarchy Computer hardware Multithreading open source software Store management Machine Learning Performance Evaluation Artificial Intelligence Functional training

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：