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Enhanced radial basis function neural networks for ozone level estimation

NEUROCOMPUTING 2015年 155卷 62-70页

作者： Ha, Q. P. Wahid, H. Duc, H. Azzi, M. Univ Technol Sydney Fac Engn & Informat Technol Broadway NSW 2007 Australia Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Malaysia Off Environm & Heritage Lidcombe NSW 1825 Australia CSIRO Energy Technol N Ryde NSW 1670 Australia

Assessment of air pollutant profiles by using measurements involves some limitations in the implementation. For this, deterministic air quality models are often used. However, its simulation usually needs high computational requirements due to complex chemical reactions involved. In this paper, a neural network-based metamodel approach is used in conjunction with a deterministic model and some measured data to approximate the non-linear ozone concentration relationship. For this, algorithms for performance enhancement of a radial basis function neural network (RBFNN) are developed. The proposed method is then applied to estimate the spatial distribution of ozone concentrations in the Sydney basin. The experimental comparison between the proposed RBFNN algorithm and the conventional RBFNN algorithm demonstrates the effectiveness and efficiency in estimating the spatial distribution of ozone level. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Ozone Radial basis function network learning algorithm Metamodel Spatial distribution

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Time series forecasting with a neuro-fuzzy modeling scheme

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APPLIED SOFT COMPUTING 2015年 32卷 481-493页

作者： Peng, Hung-Wen Wu, Shen-Fu Wei, Chia-Ching Lee, Shie-Jue Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804 Taiwan

Time series forecasting concerns the prediction of future values based on the observations previously taken at equally spaced time points. Statistical methods have been extensively applied in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose an approach based on neuro-fuzzy modeling for time series prediction. Given a predicting sequence, the local context of the sequence is located in the series of the observed data. Proper lags of relevant variables are selected and training patterns are extracted. Based on the extracted training patterns, a set of TSK fuzzy rules are constructed and the parameters involved in the rules are refined by a hybrid learning algorithm. The refined fuzzy rules are then used for prediction. Our approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate prediction. It also works for one-step as well as multi-step prediction. Several experiments are conducted to demonstrate the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Time series prediction Neuro-fuzzy modeling TSK fuzzy rules learning algorithm Training patterns

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Try again till you are satisfied:convergence,outcomes and mean-field limits

Try again till you are satisfied:convergence,outcomes and me...

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第28届中国控制与决策会议

作者： Alain Tcheukam Hamidou Tembine Learning and Game Theory Lab New York University

ISBN: (纸本)9781467397155

This article examines the famous distributed algorithm:try-again-till-you're-satisfied in opinion formation *** illustrates that a simple learning algorithm which consists to react only when unsatisfied through on/off observation can provide a satisfactory *** takes place during the interactions of the game,in which the agents have no direct knowledge of the payoff *** agent is allowed to observe their own satisfaction/dissatisfaction state and has only one-step *** existing results linking the outcomes to stationary satisfactory set do not apply to this situation because of continuous action *** provide a direct proof of convergence of the scheme for arbitrary initial conditions and arbitrary number of *** the number of iterations grows,we show that there is an emergence of a consensus in terms of opinion distribution of satisfied agents.A similar result holds for the mean-field opinion formation game.

关键词： learning algorithm model-free optimization opinion dynamics

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Improvements on twin parametric-margin support vector machine

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NEUROCOMPUTING 2015年第Part2期151卷 857-863页

作者： Peng, Xinjun Kong, Lingyan Chen, Dongjing Shanghai Normal Univ Dept Math Shanghai 200234 Peoples R China Shanghai Univ Sci Comp Key Lab Shanghai 200234 Peoples R China

Twin parametric-margin support vector machine (TPMSVM) obtains a significant performance. However, its decision function loses the sparsity, which causes the prediction speed to be much slow. In this brief, we present an improved TPMSVM, named centroid-based twin parametric-margin support vector machine (CTPSVM). The significant advantage of CTPSVM over twin support vector machine (TWSVM) and TPMSVM is that its decision hyperplane is sparse by optimizing simultaneously the projection values of the centroid points of two classes on its pair of nonparallel hyperplanes. In addition, a learning algorithm based on the clipping strategy is proposed to solve the optimization problems. Experimental results show the effectiveness of our method in speed, sparsity and accuracy, and therefore confirm further the above conclusion. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Support vector machine Parametric margin Sparsity Centroid points learning algorithm

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Trust Based Dynamic Bandwidth Allocation Scheme for Ethernet Passive Optical Networks

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WIRELESS PERSONAL COMMUNICATIONS 2015年第4期83卷 2869-2882页

作者： Kim, Sungwook Sogang Univ Dept Comp Sci Seoul 121742 South Korea

Recently, Ethernet Passive Optical Network (EPON) is a significant solution in the access layer of network that can provide large amount of bandwidth for different kind of traffic. In this paper, we propose a dynamic bandwidth allocation scheme based on the trust based VCG-Kelly mechanism. To enhance the EPON system performance, the proposed scheme can take into account the measure of each ONU's payoff variation succeeding at a given bandwidth allocation. By considering the current outcome, ONUs adaptively select their bid strategies and the OLT dynamically allocates bandwidth to reach a certain desired EPON outcome. In addition, we also consider the network security situation, which reflects what is happening in the EPON network including both the offense and defense behaviors. During this iterative learning approach, the EPON system can converge to a stable network state. Evidences from simulations demonstrate that the proposed scheme outperforms the existing schemes.

关键词： Ethernet Passive Optical Network Dynamic bandwidth allocation Trust evaluation learning algorithm VCG-Kelly mechanism Security situational awareness

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Application of Optimized Neural Network In Data Processing

Application of Optimized Neural Network In Data Processing

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作者： Jianjun WU Modern Education Technology Center Hunan City University

As the ART2 neural network clustering occurs normalization in the data inputting mode by vector and nonlinear transformation pretreatment process is easy to be filtered as a substrate for an important, but a minor component of the noise, while there are still phenomenon of the drifting mode in the learning process due to the correction of the value of weight, this paper proposes an improved method of ART2 neural network. The improved method stores the amplitude information in the learning process, and it is considering the shortest distance of being inputted into the center of the cluster, increasing a threshold limit value for determining outliers at the same time and eliminating the influence of outliers of the clustering results. Finally, the clustering of data samples experimental results show that: the improved ART2 network can handle negative data, the four quadrants of data can be effectively clustered, the performance is superior to the traditional ART2 network.

关键词： Adaptive resonance learning algorithm Neurons Resonance

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Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2015年第7期14卷 3614-3629页

作者： Dinh Thai Hoang Niyato, Dusit Wang, Ping Kim, Dong In Nanyang Technol Univ Singapore 639798 Singapore Sungkyunkwan Univ Seoul 440746 South Korea

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.

关键词： RF energy harvesting cognitive radio Markov decision process decentralized systems learning algorithm

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Solving fully fuzzy polynomials using feed-back neural networks

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2015年第4期92卷 742-755页

作者： Jafarian, Ahmad Jafari, Raheleh Golmankhaneh, Alireza Khalili Baleanu, Dumitru Islamic Azad Univ Urmia Branch Dept Math Orumiyeh Iran Islamic Azad Univ Sci & Res Branch Dept Math Arak Iran Islamic Azad Univ Urmia Branch Dept Phys Orumiyeh Iran Cankaya Univ Dept Math & Comp Sci TR-06530 Ankara Turkey Inst Space Sci R-76900 Magurele Romania King Abdulaziz Univ Fac Engn Dept Chem & Mat Engn Jeddah 21413 Saudi Arabia

Recently, there has been a considerable amount of interest and practice in solving many problems of several applied fields by fuzzy polynomials. In this paper, we have designed an artificial fuzzified feed-back neural network. With this design, we are able to find a solution of fully fuzzy polynomial with degree n. This neural network can get a fuzzy vector as an input, and calculates its corresponding fuzzy output. It is clear that the input–output relation for each unit of fuzzy neural network is defined by the extension principle of Zadeh. In this work, a cost function is also defined for the level sets of fuzzy output and fuzzy target. Next a learning algorithm based on the gradient descent method will be defined that can adjust the fuzzy connection weights. Finally, our approach is illustrated by computer simulations on numerical examples. It is worthwhile to mention that application of this method in fluid mechanics has been shown by an example.

关键词： 04A72 03E72 92B20 cost function fully fuzzy polynomials learning algorithm fuzzy feed-back neural networks fuzzy feed-forward neural networks

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Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

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APPLIED SOFT COMPUTING 2015年 37卷 667-679页

作者： Chen, Ye Mazlack, Lawrence J. Minai, Ali A. Lu, Long J. Cincinnati Childrens Hosp Res Fdn Div Biomed Informat Cincinnati OH 45229 USA Univ Cincinnati Sch Elect & Comp Syst Cincinnati OH 45221 USA Univ Cincinnati Sch Comp Sci & Informat Cincinnati OH 45221 USA Univ Cincinnati Dept Environm Hlth Coll Med Cincinnati OH 45267 USA

Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithms Fuzzy cognitive map Gene regulatory networks learning algorithm Optimization

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Comparative analysis and assessment of ANFIS-based domestic lighting profile modelling

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ENERGY AND BUILDINGS 2015年 107卷 294-306页

作者： Popoola, O. Munda, J. Mpanda, A. Popoola, A. P. I. Tshwane Univ Technol Ctr Energy & Elect Power Dept Elect Engn ZA-0001 Pretoria South Africa

A good number of stimulations tools reproduce the deterministic physical behaviour of buildings especially in lighting with repeated standard patterns of occupant presence without replicating the dynamic occupancy and activities associated with such environment. This thereby contributes to peak energy/demand crisis being experienced in countries and over estimation of energy savings associated with energy (lighting) efficient projects that have been embarked upon by utilities or government. This research work involves the comparative and performance assessment studies of an ANFIS-based model that is capable of addressing and solving non-linear issues, ambiguity and randomness of data to ensure adept estimation and prediction of lighting load profiles. The proposed technique is based on learning and adaptation of the variables associated with lighting usage. Two different investigative approaches were applied in relation to the ANFIS-based model for domestic lighting profile development. Validation process was carried out in terms of the model profiles to ascertain the performance of the methodology. Good correlation and coefficient of determination in comparison with the actual output;better correlation in comparison with other research studies and models was also deduced. This technique is expected to assist utilities, energy and measurement and verification practitioners as well as contribute to lighting load profile modelling. (C) 2015 Elsevier B.V. All rights reserved.

关键词： learning algorithm ANFIS Correlation analysis Comparative analysis Profile development Non-linear

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