A linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm

In this study, the linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.     

基于动态记忆反馈的改进ELM故障预测方法应用研究

随着工业系统复杂性的逐步增加,对故障预测的实时性和准确性提出了更高的要求.对此,提出一种基于动态记忆反馈的改进ELM神经网络模型进行故障预测.此模型在结构上增加了反馈层用于记忆隐含层输出,并从反馈层记忆的信息中提取数据变化趋势特征,从而动态更新反馈层的输出权值.通过对非线性动态系统的下一时刻输出进行预测,并对预测输出进行诊断,达到故障预测的目的.通过人工数据Sinc验证和TE过程实例应用表明了所提出方法具有预测精度高、动态适应能力强等优点,对非线性时序系统具有较好的预测能力.     

Feedback noncausal model predictive control of wave energy converters

In this paper, a novel feedback noncausal model predictive control (MPC) strategy for sea wave energy converters (WECs) is proposed, where the wave prediction information can be explicitly incorporated into the MPC strategy to improve the WEC control performance. The main novelties of the MPC strategy proposed in this paper include: (i) the recursive feasibility and robust constraints satisfaction are guaranteed without a significant increase in the computational burden; (ii) the information of short-term wave prediction is incorporated into the feedback noncausal MPC method to maximise the potential energy output; (iii) the sea condition for the WEC to safely operate in can be explicitly calculated. The proposed feedback noncausal MPC algorithm can also be extended to a wide class of control design problems, especially to the energy maximisation problems with constraints to be satisfied and subject to persistent but predictable disturbances. Numerical simulations are provided to show the efficacy of the proposed feedback noncausal MPC.     

Some hybrid aggregating operators in linguistic decision making with Dempster–Shafer belief structure

In this paper, we have studied the Dempster–Shafer theory of evidence in situations of decision making with linguistic information and we develop a new aggregation operator: belief structure generalized linguistic hybrid averaging (BS-GLHA) operator and a wide range of particular cases. we have developed the new decision making model with Dempster–Shafer belief structure that uses linguistic information in order to manage uncertain situations that cannot be managed in a probabilistic way. We have seen that all these approaches are very useful for representing the new approaches in a more complete way selecting for each situation the particular case that it is closest to our interests in the specific problem analyzed. Finally, a numerical example is used to illustrate the applicability and effectiveness of the proposed method. We have pointed out that the results and decisions are dependent on the linguistic aggregation operator used in the decision making process.     

Microplanning with Communicative Intentions: The SPUD System

The process of microplanning in natural language generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain‐specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain‐specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree‐adjoining grammar (LTAG) formalism to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptual resources which the generator can use to achieve desired microplanning behavior in a specified domain. In describing our approach to microplanning, we emphasize that we are in fact realizing a deliberative process of goal‐directed activity. As we formulate it, interpretation offers a declarative representation of a generator's communicative intent. It associates the concrete linguistic structure planned by the generator with inferences that show how the meaning of that structure communicates needed information about some application domain in the current discourse context. Thus, interpretations are plans that the microplanner constructs and outputs. At the same time, communicative intent representations provide a rich and uniform resource for the process of NLG. Using representations of communicative intent, a generator can augment the syntax, semantics, and pragmatics of an incomplete sentence simultaneously, and can work incrementally toward solutions for the various problems of microplanning.     

Predicting audio step feedback for real walking in virtual environments

When navigating in virtual environments by using real walking, the correct auditory step feedback is usually ignored, although this could give more information to the user about the ground he is walking on. One reason for this is time constraints that hinder a replay of a walking sound synchronous to the haptic step feedback when walking. In order to add a matching step feedback to virtual environments, this paper introduces a calibration‐free system, which can predict the occurrence time of a step‐down event based on an analysis of the user's gait. For detecting reliable characteristics of the gait, accelerometers and gyroscopes are used, which are mounted on the user's foot. Because the proposed system is capable of detecting the characteristic events in the foot's swing phase, it allows a prediction that gives enough time to replay sound synchronous to the haptic sensation of walking. In order to find the best prediction regarding prediction time and accuracy, data gathered in an experiment is analyzed regarding reliably occurring characteristics in the human gait. Based on this, a suitable prediction algorithm is proposed. Copyright © 2014 John Wiley & Sons, Ltd.     

海杂波背景下的小信号检测

利用海杂波信号为混沌这一先验知识,将判决反馈RBF神经网络应用于海杂波信号的建模与预测中,设计了一个三层判决反馈RBF神经网络结构.实验结果表明,判决反馈RBF网络模型对混沌时间序列有很好的拟合能力,对比典型RBF网络结构,该方法具有较好的检测效果.     

基于最大互信息的混沌时间序列多步预测

针对混沌时间序列的多步预测,提出了基于最大互信息（MMI）的建模方法.首先建立时间延迟、嵌入维数和预测步长在相空间的最大信息量模型;然后利用遗传算法求解并确定混沌时间序列的最佳预测结构;最后对Mackey-Glass系统和月太阳黑子的仿真实验表明,MMI可以确定更好的预测结构,提高了混沌时间序列的预测精度.     

Advanced P-Tree based K-Nearest neighbors for customer preference reasoning analysis

This paper presents an Advanced P-Tree based K-Nearest Neighbor (AP-KNN) algorithm for text categorization to capture useful information from customer open-end answers via an intelligent survey system. The “intelligence” of this survey system is built in its text categorization module which can classify customers’ feedbacks on certain characteristics of the products in concern. A software prototype system is developed based on the AP-KNN algorithm. The prototype system allows online questionnaire design, online customer feedback collection, digitisation of linguistic feedback and customer preference reasoning and motivation analysis. The system could significantly shorten the survey and analysis time and is thus expected to reduce design cycle time for new product development. To test the AP-KNN, a case study was carried out in a survey for developing portable audio products. The study shows that AP-KNN performed much better than the original P-Tree based KNN in terms of speed and accuracy.     

Pythagorean linguistic preference relations and their applications to group decision making using group recommendations based on consistency matrices and feedback mechanism

In this paper, we introduce a new type of fuzzy set, called Pythagorean linguistic sets (PLSs), to address the preferred and nonpreferred degrees of linguistic variables. Moreover, it allows decision makers to offer effectively handle uncertain information more flexible than intuitionistic linguistic sets (ILSs) when one compares two alternatives in the process of decision making. Some of the fundamental operational laws, score, accuracy, and aggregation operators are defined, and their properties are investigated. Preference relation (PR) is a useful and efficient tool for decision making that only requires the decision makers to compare two alternatives at one time. Taking the advantages of PLSs and PRs, this paper also introduces Pythagorean linguistic preference relations (PLPRs) and studies their application. We propose an approach for group decision making using group recommendations based on consistency matrices and feedback mechanism. First, the proposed method constructs the collective consistency matrix, the weight collective PRs, and the group collective PRs. Then, it constructs a consensus relation for each expert and determines the group consensus degree (GCD) for all experts. If the GCD is smaller than a predefined threshold value, then a feedback mechanism is activated to update the PLPRs. Finally, after the GCD is greater than or equal to the predefined threshold value, we calculate the arithmetic mathematical average values of the updated group collective PR to select the most appropriate alternative.     

Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks

We propose to fit a recurrent feedback neural network structure to input–output data through prediction error minimization. The recurrent feedback neural network structure takes the form of a nonlinear state estimator, which can compactly represent a multivariable dynamic system with stochastic inputs. The inclusion of the feedback error term as an input to the model allows the user to update the model based on feedback measurements in real-time uses. The model can be useful in a variety of applications including software sensing, process monitoring, and predictive control. A dynamic learning algorithm for training the recurrent neural network has been developed. Through some examples, we evaluate the efficacy of the proposed method and the prediction improvement achieved by the inclusion of the feedback error term.     

Interactions between linguistic constraints: Procedural vs. declarative approaches

The traditional approach to generation is to derive a surface string from a semantic structure through various intermediate levels using a carefully ordered set of transformation steps. We show by some examples that this approach involves a lot of specific control decisions which cannot be generalized across several languages. We present a constraint-based approach where all levels of linguistic information are represented in a single structure. All levels introduce constraints on the linguistic structure stated as a set of feature type definitions. Relationships between levels are modelled as a set of (partial) relational constraints which apply simultaneously on all levels of the linguistic structure.Research reported in this paper is partly supported by the German Ministry of Research and Technology (BMFT, Bundesminister für Forschung und Technologie), under grant No. 08 B3116 3. The views and conclusions contained herein are those of the authors and should not be interpreted as representing official policies. We would like to thank John Bateman for his helpful comments on a previous version of this paper. The responsibility for all remaining errors resides, of course, with the authors.     

基于字单元分析的中文辅助阅读系统

辅助汉语学习研究作为一个重要的研究领域,已经在自然语言处理领域激发起越来越多人的兴趣。文中提出一个基于字分析单元的辅助阅读系统,它可以为汉语学习者提供即时的辅助翻译和学习功能。系统首先提出基于字信息的汉语词法分析方法,对汉语网页中文本进行分词处理,然后利用基于组成字结构信息的方法发现新词。对于通用词典未收录的新词(例如: 专业术语、专有名词和固定短语),系统提出了基于语义预测和反馈学习的方法在Web上挖掘出地道的译文。对于常用词,系统通过汉英(或汉日)词典提供即时的译文显示,用户也可通过词用法检索模块在网络上检索到该词的具体用法实例。该系统关键技术包括: 基于字信息的汉语词法分析,基于组成字结构信息的新词发现,基于语义预测和反馈学习的新词译文获取,这些模块均以字分析单元的方法为主线,并始终贯穿着整个系统。实验表明该系统在各方面都具有良好的性能。     

Decentralised memory static output feedback control for the nonlinear time-delay similar interconnected systems

In this paper, the problem of decentralised memory static output feedback control for a class of nonlinear time-delayed interconnected systems with similar structure is investigated, where both the linear and nonlinear state vectors involve time delay. The contributions of the paper include the following: (1) a new similar structure is presented via memory static output feedback; (2) by exploiting the structure of interconnected systems, the new integral inequalities, constrained Lyapunov equations and LMI method, the decentralised memory static output derivative feedback controllers with similar structure are designed, which is dependent of time delays, to stabilise the interconnected systems uniformly asymptotically; and (3) the stability domain is estimated. The conservatism of the results obtained is reduced by full using the system output information. Finally, the numerical examples are given to demonstrate the effectiveness of the results obtained in this paper.     

A method for multiple attribute decision making with incomplete weight information in linguistic setting

The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We first introduce some approaches to obtaining the weight information of attributes, and then establish an optimization model based on the ideal point of attribute values, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the numerical weighting linguistic average (NWLA) operator to aggregate the linguistic variables corresponding to each alternative, and then rank the alternatives by means of the aggregated linguistic information. Finally, the developed method is applied to the ranking and selection of propulsion/manoeuvring system of a double-ended passenger ferry.     

A new model for linguistic summarization of heterogeneous data: an application to tourism web data sources

In this paper we present the problem of aggregating heterogeneous data from various websites with opinions about high end hotels into a database. We present the fuzzy model based on the semantic translation as a tool to obtain a linguistic summarization. The characteristics of this model (necessary to solve the problem) are not together on any of the existing linguistic models: the management of the input heterogeneous data (natural language included); the procurement of linguistic results with high precision and good interpretability; and the use of unbalanced linguistic term sets described by trapezoidal membership functions for defining the initial linguistic terms. We applied it to aggregate data from certain high end hotels websites and we show a case study using the high end hotels located in Granada (Spain) from such websites during a year. With this aggregated information, a data analyst can make several analyses with the benefit of easy linguistic interpretability and a high precision. The solution proposed here can be used to similar aggregation problems.     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.     

Linguistic time series forecasting using fuzzy recurrent neural network

It is known that one of the most spread forecasting methods is the time series analysis. A weakness of traditional crisp time series forecasting methods is that they process only measurement based numerical information and cannot deal with the perception-based historical data represented by linguistic values. Application of a new class of time series, a fuzzy time series whose values are linguistic values, can overcome the mentioned weakness of traditional forecasting methods. In this paper we propose a fuzzy recurrent neural network (FRNN) based time series forecasting method for solving forecasting problems in which the data can be presented as perceptions and described by fuzzy numbers. The FRNN allows effectively handle fuzzy time series to apply human expertise throughout the forecasting procedure and demonstrates more adequate forecasting results. Recurrent links in FRNN also allow for simplification of the overall network structure (size) and forecasting procedure. Genetic algorithm-based procedure is used for training the FRNN. The effectiveness of the proposed fuzzy time series forecasting method is tested on the benchmark examples.     

A 2-tuple fuzzy linguistic representation model for computing withwords

The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model