The past three decades have witnessed active research using a class of Artificial Neural Network models (ANN), the Radial Basis Function Neural Networks, to forecast time series. Many techniques for forecasting time s...
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ISBN:
(纸本)9781450364126
The past three decades have witnessed active research using a class of Artificial Neural Network models (ANN), the Radial Basis Function Neural Networks, to forecast time series. Many techniques for forecasting time series using Radial Basis Function Neural Networks (RBFNN) have been proposed and developed in literature. The major challenges in RBFNN lie in the optimization of its full parameters: the number and location of cluster centres as well as the output weights. To address these challenges, this study adapted the Clustering Analysis based on glowwormswarmoptimization (CGSO) algorithm to obtain a modified Clustering Analysis based on glowwormswarmoptimization (CGSOm) algorithm for solving the clustering problem. Adaptation was achieved by incorporating a mechanism that determines the sensor range of the CGSO efficiently and automatically, and modifying the glowworm initialization method. For the weight optimization, the Bioluminescence swarmoptimizationalgorithm (BSO) was adopted, making it the first time it will be applied in training the weights of the RBFNN. Other training algorithms tested include Conjugate Gradient Descent (CGD), Gradient Descent (GD) and Particle swarmoptimizationalgorithm (PSO). Stock price and currency exchange rate data were used to train the combinations of models developed. The results obtained from the training showed that the CGSOm-CGD RBFNN gave best forecasting accuracy by yielding lowest error values;followed by the CGSOm-BSO RBFNN that gave relatively similar error values. Hence, two new training methodologies for time series forecasting resulted from this study;they are the CGSOm-BSO RBFNN and the CGSOm-CGD RBFNN.
Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to pre...
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Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of nature inspired optimizationalgorithm known as glowwormswarmoptimization (GSO) algorithm. The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive). The proposed algorithm is tested on IEEE 14, 30, 57, 118 and Northern Regional Power Grid (NRPG)-(India) 246 bus test systems. The viability of the proposed method in terms of solution quality and convergence properties is compared with the conventional methods, namely, projected augmented Lagrangian method (PALM), gradient technique based on Kuhn-Tucker theorem (GTBKTT) and second order gradient technique (SOGT). (C) 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.
Recent days, cloud computing is an improving area in research and industry, which includes virtualization, distributed computing, internet, and web services. A cloud contains elements such as data centers, clients, di...
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ISBN:
(纸本)9781479939664
Recent days, cloud computing is an improving area in research and industry, which includes virtualization, distributed computing, internet, and web services. A cloud contains elements such as data centers, clients, distributed servers, internet which includes fault tolerance, high accessibility, efficiency, scalability, flexibility, reduced overhead for users, less cost of ownership, on demand services and etc. Cloud computing services are becoming omnipresent and serve as the primary source of calculating power for different applications like activity and personal computing applications. It has many benefits all along with some fundamental problems to be resolved in order to improve reliability of cloud environment. Also that, these problems is associated with load management, tolerance and different security issues in cloud environment. In this paper introduces a better load balancing model for the public cloud based on the cloud partitioning concept with a switch mechanism to select different strategies for different situations. Adaptive neuro-fuzzy inference system (ANFIS) based load balancing algorithm and glowwormswarmoptimization (GSO) based load balancing algorithm are proposed to the load balancing strategy to improve the efficiency in the public cloud environment. Experimental result shows that the proposed method gives better results when compared with other traditional methods.
glowwormswarmoptimization (GSO) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhances accuracy and convergence rate of the GSO, two strategies about the moveme...
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glowwormswarmoptimization (GSO) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhances accuracy and convergence rate of the GSO, two strategies about the movement phase of GSO are proposed. One is the greedy acceptance criteria for the glowworms update their position one-dimension by one-dimension. The other is the new movement formulas which are inspired by artificial bee colony algorithm (ABC) and particle swarmoptimization (PSO). To compare and analyze the performance of our proposed improvement GSO, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the improvement algorithms are discussed by uniform design experiment. Numerical results reveal that the proposed algorithms can find better solutions when compared to classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems. (C) 2011 Elsevier Ltd. All rights reserved.
To solve the problem of oil chromatographic on-line data distortion caused by outside environment and equipment error, a method for the oil chromatographic on-line data reconciliation based on glowwormswarm optimizat...
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ISBN:
(纸本)9781467363495
To solve the problem of oil chromatographic on-line data distortion caused by outside environment and equipment error, a method for the oil chromatographic on-line data reconciliation based on glowwormswarmoptimization (GSO) and support vector machine (SVM) is presented. Firstly, the important parameters that affect the performance of SVM are optimized through GSO. Secondly, SVM regression model is trained by some precise oil chromatographic off-line data. Then the oil chromatographic on-line data is reconciled by SVM regression model when the on-line data is abnormal. Finally, the feasibility and efficiency of the method proposed in the paper is confirmed by the oil chromatographic on-line and off-line data of the power transformer.
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