In the research work on blocking flow, the concept of minimum flow of a two‐terminal network was introduced. In this paper, a kind of special blocking flow – the concept of…
Abstract
In the research work on blocking flow, the concept of minimum flow of a two‐terminal network was introduced. In this paper, a kind of special blocking flow – the concept of minimum spanning flow in the network is introduced and its construction method studied. Here, we show that it is easy to determine whether there is a minimum spanning flow in a network in polynomial time, but it is hard to determine whether there is a non‐circuit minimum spanning flow in one step. Fortunately, the latter problem can be solved in two steps, and its self‐organizing principle is put forward. The feasibility of the algorithm developed on this principle was proved by about 4,500 examples. The significance of this research work is pointed at last.
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Dongqing Zhang, Xuanxi Ning and Xueni Liu
As the conventional multistep‐ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions…
Abstract
Purpose
As the conventional multistep‐ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions, which provides the most probable estimation for the predicted trajectory.
Design/methodology/approach
Many real‐time series can be modeled in hidden Markov models. In order to predict these time series online, sequential Monte Carlo (SMC) method is applied for joint multistep‐ahead prediction.
Findings
The data of monthly national air passengers in China are analyzed, and the experimental results demonstrate that the method proposed and the corresponding online algorithms are effective.
Research limitations/implications
In this paper, SMC method is applied for joint multistep‐ahead prediction. However, with the increasing of prediction step, the number of particles is increasing exponentially, which means that the prediction steps cannot be too large.
Practical implications
A very useful advice for researchers who study time‐series forecasts.
Originality/value
A novel method of multistep‐ahead prediction based on joint probability distribution is proposed and SMC method is applied to prediction time series online. This paper is aimed at those researchers who focus on time‐series forecasts.
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Dongxiao Niu, Ling Ji, Yongli Wang and Da Liu
The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network…
Abstract
Purpose
The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.
Design/methodology/approach
The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.
Findings
The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.
Originality/value
The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.