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检索条件"主题词=learning algorithm"

共 748 条记录，以下是231-240 订阅

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An estimation framework in a forward-propagation learning rule

An estimation framework in a forward-propagation learning ru...

引用

SICE 2004 Annual Conference

作者： Ohama, Y Fukumura, N Uno, Y Toyohashi Univ Technol Toyohashi Aichi 4418580 Japan

ISBN: (纸本)4907764227

A forward-propagation learning rule has been proposed to acquire neural inverse models. This rule can solve a credit assignment problem based on Newton-like method. In the current work, we discuss how to estimate the parameters of a multi-layered neural network based on the credit assignment. The suitability of the proposed estimation framework is confirmed by computer simulation.

关键词： inverse model layered neural network learning algorithm credit assignment problem

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learning-Based Joint Power and Channel Assignment for Hyper Dense 5G Networks

Learning-Based Joint Power and Channel Assignment for Hyper ...

引用

IEEE International Conference on Communications (ICC)

作者： Arani, Atefeh Hajijamali Mehbodniya, Abolfazl Omidi, Mohammad Javad Adachit, Fumiyuki Isfahan Univ Technol Dept Elect & Comp Engn Esfahan 84156 Iran Tohoku Univ Grad Sch Engn Dept Commun Engn Sendai Miyagi Japan

ISBN: (纸本)9781479966646

Next generation mobile networks will face the unprecedented demand for higher data rates. To satisfy this demand, the dense deployment of heterogeneous wireless networks (HetNets) is a promising solution. One of the major challenges in dense HetNets is to dynamically allocate the resources such as power and channel so that the energy efficiency and throughput of the network improve. One of the important techniques for improving the energy efficiency of the base station (BS) is BS ON-OFF switching which allows the BS to turn off some of its components in lower load situations. On the other side, due to the proximity of BSs in the dense HetNets, co-channel interference (CCI) becomes a critical problem and significantly impacts the performance of the network. In this paper, we propose a dynamic channel assignment based on a learning algorithm (DCA-LA). Moreover, we combine DCA-LA with a BS ON-OFF switching algorithm in order to improve the energy efficiency of the system. In particular, the proposed DCA-LA/ON-OFF switching algorithm is self-organizing and performs in a fully distributed manner. Simulation results indicate that our proposed algorithm balances load among BSs and yields better performance in terms of average energy consumption, average load, average utility per BS and average rate per user, compared to the baseline algorithms.

关键词： Heterogeneous Networks Energy Efficiency Co-Channel Interference learning algorithm

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Training of Interval Type-2 Fuzzy Logic System using Extreme learning Machine for Load Forecasting 9

Training of Interval Type-2 Fuzzy Logic System using Extreme...

引用

9th International Conference on Ubiquitous Information Management and Communication (ACM IMCOM)

作者： Hassan, Saima Khosravi, Abbas Jaafar, Jafreezal Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskander Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia KUST Inst IT Kohat Pakistan

ISBN: (纸本)9781450333771

Extreme learning machine (ELM) is originally proposed for single-hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

关键词： Extreme learning machine interval type-2 fuzzy logic systems learning algorithm load forecasting parameter optimization

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Extended Extreme learning Machine: A Novel Framework for Neural Network

Extended Extreme Learning Machine: A Novel Framework for Neu...

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jiaramaneepinit, Boonnithi Nuthong, Chaiwat King Mongkuts Inst Technol Ladkrabang Int Coll Bangkok Thailand

ISBN: (纸本)9781538666500

Machine learning has been a focus research topic of superior tasks in many real-world applications. One of the famous preferred system is neural network. This approach has been invented for decades but becomes popular recently due to its satisfied results in many applications. The success of applying neural network involves model training which conventionally uses backpropagation method. However, it has many drawbacks. In recent decades, Extreme learning machine (ELM) was first proposed for training single-hidden layer feedforward neural network (SLFN). It optimizes training error by utilizing the whole training dataset with a one-shot calculation. However, for the training in datasets with large number of input features or high dimensional datasets, original ELM encounters many difficulties. One of them is that the original ELM has no learning process from an input layer. This lead to an incomplete representation of data when it is transferred from one layer to another. Another difficulty involves training instability which causes fluctuation in testing accuracy. This is because networks' input weights are randomly generated. To circumvent these difficulties, the imposing architecture, namely Extended Extreme learning Machine (X-ELM), is proposed. X-ELM uses ELM as an extension part in order to predict the outputs based on ensemble approach. The proposed framework extends the usage of ELM to apply to more complex network structures, such as networks with multiple hidden layers or networks with multiple computing systems. The proposed framework is applied to vehicles characteristic classifications' datasets. The experimental results show that X-ELM achieves better testing accuracy than of ELM in real-world applications.

关键词： machine learning network architecture artificial neural network single-hidden layer feedforward neural network multi-layer networks predictor classifier classification framework learning theory learning algorithm fast learning real-time learning backpropagation Extreme learning Machine Extended Extreme learning Machine

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PREVIEW ON STRUCTURES AND algorithmS OF DEEP learning 11

PREVIEW ON STRUCTURES AND ALGORITHMS OF DEEP LEARNING

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11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Kuang, Ping Cao, Wei-Na Wu, Qiao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781479972081

Deep learning proposed by Hinton et al is a new learning algorithm of multi-layer neural network, and it is also a new study field in machine learning. This paper describes the structures and advantages to shallow learning of deep learning, and analyzes current popular learning algorithm in detail. Finally, this paper analyzes research directions and future prospects of deep learning.

关键词： Deep learning learning algorithm machine learning

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Hybrid Multidimensional Wavelet-Neuro-System and its learning Using Cross Entropy Cost Function in Pattern Recognition 2

Hybrid Multidimensional Wavelet-Neuro-System and its Learnin...

引用

2nd IEEE International Conference on Data Stream Mining and Processing (DSMP)

作者： Vynokurova, Olena Peleshko, Dmytro Borzov, Yuriy Oskerko, Semen Voloshyn, Viktor IT Step Univ Lvov Ukraine Lviv State Univ Life Safety Dept Project Management Informat Technol & Teleco Lvov Ukraine

ISBN: (纸本)9781538628744

In this paper, the hybrid multidimensional wavelet-neuro-system for pattern recognition tasks is proposed. Also learning algorithm for all its parameters (synaptic weights, the centers, and widths of wavelet activation functions) based on cross entropy cost function was proposed. The proposed system is characterized by high learning speed and high approximation properties in comparison with well-known approaches. The efficiency of the proposed approach has been justified based on different benchmarks and real data sets.

关键词： patterns recognition hybrid wavelet-neuro-system learning algorithm wavelet transform cross entropy cost function

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Equilibrium Point learning 15

Equilibrium Point Learning

引用

15th Asian Conference on Machine learning (ACML)

作者： Baik, Dowoo Yoon, Ji Won Naval Acad Kannur Kerala India Korea Univ Seoul South Korea

We present a novel approach, Equilibrium Point learning (EPL), for training the deep equilibrium model (DEQ). In this method, the equilibrium point of the DEQ serves as the learnable parameters. Notably, the DEQ parameters encapsulate the learning algorithm itself and remain fixed. Consequently, by exploring the parameter space, we can discover a more efficient learning algorithm without relying on conventional techniques such as backpropagation or Q-learning. In this paper, we adopt an evolutionary approach inspired by biological neurons to evolve the DEQ model parameters. Initially, we examine the physical dynamics of neurons at the molecular level and translate them into a dynamical system representation. Subsequently, we formulate a deep implicit layer that is mathematically proven to possess an equilibrium point. The energy function of the implicit layer is defined using a quadratic form augmented with entropy and momentum terms. Given the resemblance between the dynamics of the deep implicit layer and the principles of physics and chemistry, it can effectively capture the biomodel of systems biology and the neural model of spiking neural networks (SNNs). This equivalence enables us to define the implicit layer of the DEQ, allowing for seamless integration with existing artificial neural networks (ANNs). Finally, we employ HyperNEAT to evolve the parameters of the dynamical system. Through our experiments, we observe a consistent improvement in learning efficiency, with each successive generation exhibiting a 0.2% increase in learning speed per generation. Keywords: Deep equilibrium model, learning algorithm, Biomodel, HyperNEAT

关键词： Deep equilibrium model learning algorithm Biomodel HyperNEAT

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Comparative Study of learning Effectiveness Across NEPs on Network Fault Correlation 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Bose, Avik Natl Inst Oceanog CSIR Panaji Goa India IBM Corp Mumbai Maharashtra India

ISBN: (数字)9789819713264

ISBN: (纸本)9789819713257;9789819713264

The paper presents a comparative analysis of performance of various network equipment providers (NEPs) operating radio access network (RAN) by fault correlation using error code. Effectiveness is measured using supervisory learning algorithms applied on network alarm data derived from fault management system (FMS) along with key performance indicators (KPIs) derived from performance management system (PMS). Results are based on the ML reinforcement model algorithm is tested with live data stream for one large CSP in India with accuracy of similar to 90% using vendor [Ericsson/Nokia/Huawei] and technology [2G/3G].

关键词： Fault correlation learning algorithm

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Knowledge-Based learning for Solving Vehicle Routing Problem 18

Knowledge-Based Learning for Solving Vehicle Routing Problem

引用

ACM International Joint Conference on Pervasive and Ubiquitous Computing / ACM International Symposium on Wearable Computers (UbiComp/ISWC)

作者： Phiboonbanakit, Thananut Horanont, Teerayut Supnithi, Thepchai Van-Nam Huynh Thammasat Univ Sch Informat Comp & Commun Techno SIIT Pathum Thani Thailand Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Ishikawa Japan Natl Sci & Technol Dev Agcy NECTEC Pathum Thani Thailand

ISBN: (纸本)9781450359665

In this study, we have developed a method that applies machine learning in combination with an optimization heuristic algorithm such as a genetic algorithm (GA) for solving the vehicle routing problem (VRP). Further, we developed a knowledge-based algorithm for a knowledge learning system. The algorithm learns to classify coordinates (unlabeled) into regions. Consequently, dividing routing calculations into regions (clusters) provides many benefits over traditional methods, and can result in an improvement in routing cost over the traditional company method by up to 25.68% and over the classical GA by up to 8.10%. It is also shown that our proposed method can reduce traveling distance compared to previous methods. Finally, the prediction of future customer regions has an accuracy of up to 0.72 for the predicted unlabeled customer coordinates. This study can contribute toward creation of more efficient and environmentally friendly urban freight transportation systems.

关键词： Vehicle routing problem learning algorithm Genetic algorithms Neural networks Geolocation clustering

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learning BASED RECONFIGURABLE SUB-NYQUIST SAMPLING FRAMEWORK FOR ULTRA-WIDEBAND ANGULAR SENSING

LEARNING BASED RECONFIGURABLE SUB-NYQUIST SAMPLING FRAMEWORK...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Joshi, Himani Alaee-Kerahroodi, Mohammad Kumar, A. Anil Mysore, Bhavani Shankar R. Darak, Sumit J. IIIT Delhi ECE Dept New Delhi India Univ Luxembourg SnT Luxembourg Luxembourg TCS Innovat Lab Bangalore Karnataka India

ISBN: (纸本)9781509066315

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

关键词： learning algorithm reconfigurable sub-Nyquist sampling wideband angular sensing

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