Cournot games with linear regression expectations in oligopolistic markets

In this paper, a Cournot game in an oligopolistic market with incomplete information is considered. The market consists of some producers that compete for getting higher payoffs. For optimal decision making, each player needs to estimate its rivals’ behaviors. This estimation is carried out using linear regression and recursive weighted least-squares method. As the information of each player about its rivals increases during the game, its estimation of their reaction functions becomes more accurate. Here, it is shown that by choosing appropriate regressors for estimating the strategies of other players at each time-step of the market and using them for making the next step decision, the game will converge to its Nash equilibrium point. The simulation results for an oligopolistic market show the effectiveness of the proposed method.     

Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.     

高速列车非线性模型的极大似然辨识

提出高速列车非线性模型的极大似然(Maximum likelihood, ML)辨识方法,适合于高速列车在非高斯噪声干扰下的非线性模型的参数估计.首先,构建了描述高速列车单质点力学行为的随机离散非线性状态空间模型,并将高速列车参数的极大似然(ML)估计问题转化为期望极大(Expectation maximization, EM)的优化问题; 然后,给出高速列车状态估计的粒子滤波器和粒子平滑器的设计方法,据此构造列车的条件数学期望,并给出最大化该数学期望的梯度搜索方法,进而得到列车参数的辨识算法,分析了算法的收敛速度; 最后,进行了高速列车阻力系数估计的数值对比实验. 结果表明, 所提出的辨识方法的有效性.     

A stochastic beam search for the berth allocation problem

In this paper, the optimization of the Berth Allocation Problem (BAP) is transformed into a multiple stage decision making procedure and a new multiple stage search method, namely stochastic beam search algorithm, is proposed to solve it. New techniques such as an improved beam search scheme, a two-phase node goodness estimation, and a stochastic node selection criteria are proposed. Real-life information provided by Singapore Port was collected as our test data. Experimental results show that the proposed stochastic beam search is more accurate and efficient than both the state-of-the-art meta-heuristic and the traditional determinist beam search.     

基于前景随机占优的多属性多标度大群体决策方法

针对具有属性期望的多属性多标度大群体决策问题,在考虑参与决策人心理行为的情境下,将每个参与决策人给出的属性期望视为其参照点,并构建群体感知的收益-损失的概率分布;然后,基于前景随机占优准则建立方案比较的前景随机占优关系矩阵,并通过计算方案比较的前景随机优势度来得到方案的排序.算例分析表明了所提出方法的可行性和有效性.     

基于前景M-V准则的正态三角模糊随机多属性决策方法

针对具有正态三角模糊随机变量且属性权重未知的多属性决策问题, 提出基于前景均值-方差(M-V) 准则的正态三角模糊随机多属性决策方法. 该方法首先构建正态三角模糊随机决策矩阵, 进而通过运算得到属性值的期望与方差, 并将其转化为M-V 决策矩阵; 然后, 通过定义前景效应构建前景M-V 决策矩阵, 利用改进灰色系统理论模型求解属性权重值, 获取综合前景M-V 决策矩阵; 最后, 定义前景序关系, 两两比较前景M-V 价值获取方案排序. 在此基础上, 通过案例验证了所提出方法的可行性及有效性.

     

Automatic modeling algorithm of stochastic error for inertial sensors

This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.     

Design of input vector for day-ahead price forecasting of electricity markets

In new deregulated electricity market, price forecasts have become a fundamental input to an energy company’s decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques.     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model

On the basis of the market microstructure theory, a continuous time microstructure model is proposed for describing the dynamics of financial markets with stochastic volatility property. From the microstructure model, one may obtain the estimates of two state variables, which represent the market excess demand and liquidity respectively but cannot be directly observed. Based on the indirectly obtained excess demand information instead of the prediction of price, a simple asset dynamic allocation approach is investigated. The local linearization method, nonlinear Kalman filter and maximum likelihood method-based estimation approach for the microstructure model proposed is presented. Case studies on the financial markets modelling and the estimated model-based asset dynamic allocation control for the JPY/USD (Japanese Yen/US Dollar) exchange rate and Japan TOPIX (Tokyo stock Price IndeX) show a satisfactory modelling precision and dynamic allocation performance.     

动态联盟的一种模糊最优投资策略

针对动态联盟或虚拟企业中两个互补型合作企业, 提出了一种模糊投资决策模型, 该模型同时考虑了企业的相关性和鼓励投资的激励措施应用模糊优化技术对该模型进行求解, 确定了获得最大总利润的最优投资策略 ;并用计算实例证明了该方法的有效性. 为虚拟企业进行科学的投资决策, 提供了有益的借鉴.     

基于前景理论的农村电子商务发展水平多维偏好决策方法

现实中存在多维偏好和需要考虑专家心理行为的农村电子商务发展水平决策问题.针对这类农村电子商务发展水平决策问题,提出一种基于前景理论的多维偏好决策方法.首先,描述农村电子商务发展水平评价问题,进而计算各个决策方案的综合前景值;然后,定义基于前景理论的客观排序与专家偏好的一致性程度和不一致程度;最后,以决策者的期望水平和容忍度为基础,建立模糊多维偏好优化模型,进而计算各个决策方案的综合前景值,据此确定方案优劣排序和最优方案.通过分析三明市各个县市农村电子商务的发展水平表明了所提出方法有效合理.     

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.     

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

A prediction scheme using perceptually important points and dynamic time warping

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price.     

基于线性模型的信道质量估计算法

针对自适应卫星通信中对信道质量估计的需求,在加性高斯白噪声信道条件下,以信干噪比(SINR)作为表征信道质量的参数,提出一种信道质量估计算法.给出矩估计法和判决数据估计法的数学分析,利用指数加权因子对噪声加干扰的功率进行平滑.提出基于矩估计和判决数据估计的线性模型对SINR估计的算法,分析该模型的均方误差,同时搜索最佳...     

随机环境下编队协同对地攻防对抗决策方法研究

随着现代军事技术的发展,军事作战指挥中的不确定性决策问题成为军事决策支持系统中迫切需要解决的问题。文章以对策论为基础,编队协同对地攻防对抗作战为背景,建立了动态对抗决策模型;针对不确定性军事指挥决策中的随机问题,提出了随机影响因子概念,反映战场随机环境对各参战单元产生的影响,并建立了随机对抗决策模型。仿真结果表明,该算法能够合理地处理战场随机状况,客观分析作战结果,为作战指挥决策提供有力的决策支持,方法实用、有效,具有良好的应用前景。     

不同风险偏好下双渠道供应链定价决策

为了探讨风险偏好对双渠道供应链决策的影响, 基于条件风险值(CVaR) 准则建立双渠道供应链定价决策模型, 并给出了模型的求解方法和最优解. 研究表明, 根据不同风险偏好程度, 供应链成员会采取不同定价策略; 当制造商风险偏好程度确定、零售商风险规避度增加时, 最优零售价降低, 最优批发价升高, 直销价不减; 当零售商风险偏好度确定、制造商风险规避度增加时, 各最优价格均降低; 风险偏爱的影响则与风险规避相反.

     

Fractionally Delayed Kalman Filter

The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.