含参模糊决策蕴涵

智能决策是人工智能的重要组成部分.在形式概念分析中,决策表现为决策背景上的决策蕴涵,模糊决策蕴涵则是建立在模糊决策背景上的决策蕴涵,其前件和后件分别为条件属性和决策属性.因为模糊决策蕴涵可以避免条件属性之间和决策属性之间生成模糊属性蕴涵,因而具有更广泛的应用意义.确定化、无调节的知识获取方式对实际应用的适应性差.因此,需要将不可调节的知识发现方式拓展到参数化可调节的知识发现方式.语气真值算子和阈值作为2种参数化策略在具有模糊属性的形式概念分析中发挥着重要的作用.现有的模糊决策蕴涵模型仅考虑到语气真值算子的可变,可调节性较差,考虑阈值参数化策略的研究较少.以完备剩余格为参考框架,将语气真值算子和阈值2种参数化策略引入模糊决策蕴涵,提出含参模糊决策蕴涵,研究其语义特征,证明一些基本性质,并提出3条推理规则,证明其合理性和完备性.在实际生活中,用户可以选择合适的阈值来获取需要的知识,并且可以使用推理规则来进行知识推理,提升模糊决策蕴涵的可调节性和应用价值.

重大行政决策法治化的实现路径

"重大行政决策法治化"已经成为加快建设法治政府过程中的一个重要问题,其基本的实现路径为:激活民主参与、健全法定程序、强化合法性审查、落实决策责任。民主参与对于重大行政决策遵循法治理念起着基础性的作用。建立健全决策的程序,不仅应该抓好决策的相关环节,而且应该加快重大行政决策程序方面的立法工作。合法性审查不仅要廓清"谁审查?审查谁?"的问题,而且更要强调:如果没有经过合法性审查或者审查结果为不合法的,行政机关不得对此事项进行决策。落实决策责任需要明确责任主体、责任条件、责任种类,保障责任的实现。

基于混沌关联维与决策树的运动数据分类研究

运动数据能够为运动的监测与管理提供指导依据,由于采集设备的差异导致运动数据表现出多源、混沌、非规则等特征,现有运动数据分类方法在准确性和效率等方面仍有欠缺,因此提出了基于混沌关联维与决策树的运动数据分类方法。首先构建运动数据相空间,利用时间的自相关与互信息来确定最优混沌延时,利用虚假临界点的条件约束确定最优嵌入维度;根据相空间内存在的关联相点对数量,计算得到关联积分,从而完成数据特征提取。然后针对运动数据的高维属性设计多参决策树,根据信息增益与增益率评价确定分裂规则,根据熵期望确定最优分裂点。最后通过轮廓相似性判断数据所属类别,完成运动数据分类。基于WEKA平台的仿真结果,验证所提方法显著提高了运动数据分类的准确性,准确度达到81.47%,灵敏度达到83.92%,降低了错误分类概率,不合度仅为19.46%;另外,决策树规模与叶子数量的降低,以及吞吐量结果均表明方法具有良好的数据处理速度,有效提升了数据分类效率。

关于新型农村合作医疗制度的调研报告——以浙江省上虞市为例

新型农村合作医疗制度试行已经四年,本文在对试点县市之一的浙江省上虞市参加或未参加合作医疗的农户深入调查的基础上,综合上虞市新型合作医疗制度实施的基本状况,运用数学和经济学的相关理论,对农户参加合作医疗的影响因素进行了研究,认为农户的家庭经济状况、健康状况、健康理念、就医习惯对农户参加合作医疗决策有重要的影响。

辽宁省人民政府关于进一步加强督查工作的实施意见

辽宁省人民政府文件辽政发[2015]2号各市人民政府,省政府各厅委,各直属机构:根据《国务院办公厅关于进一步加强政府督促检查工作的意见》(国办发[2014]42号)和《辽宁省人民政府工作规则》(辽政发[2013]13号),制定本实施意见,指导全省政府督促检查抓落实工作。一、总体要求各级政府要坚决贯彻落实习近平总书记关于"一分部署,九分落实",李克强总理关于"政府要说到做到,不能放空炮"的重要指示,把督促检查作为一把"利剑"。

A Novel Model of F0 Contours Prediction for Continuous Speech

F0 (fundamental frequency) contour was studied under different prosodic environment in continuous speech and a novel model of F0 contours prediction was proposed. It describes syllabic F0 contour with two points, one curve and duration. The curve represents two optimal points of controlling parameters. The duration represents the syllabic duration. The prosodic characters of controlling parameters were analyzed by CART (Class and Regression Tree). A set of cont rolling parameters was analyzed, which reflects the linguistic environment and prosodic structure. Then it sets up the model of F0 contours prediction with the two optimal controlling parameters and F0 templates. The end pitch value of previous syllable as special prosodic parameters was used to keep the continuity of fore-and-aft syllable. It focuses on looking out the main prosodic clues hiding in F0 contours and applying it to simplify the model for prediction. The results of s ynthesis experiment show that the performance of the prediction method is apprec iated.

Participatory Governance and Decision-Making： Oil and Gas in Santos （Brazil）

The growth of industry and services occurred on a world scale in recent decades, is correlated to large events that propel whole nations to think of local development strategies, integrated and sustainable, based on structural and conjunctural changes, and brought on by human factors or natural causes in dependent of the human will, and lies in the context of innovative models of supply chain arrangements, as is the case of participatory governance network of production of oil and gas. The present study aim is identify and analyze the impacts, local and regional, due to the introduction of the production of oil and gas （originating layer known aspre-salt） in the city of Santos, Sao Paulo, Brazil. The current state of development of a software monitoring indicator is aimed at assessing the present stage and monitor the process of participatory governance in the network of production of oil and gas in Santo is presented here.

A Method for Risky Multiple Attribute Decision Making with Four - dimensional Reference Point

A method based on cumulative prospect theory was proposed to solve risky multiple attribute decision making problems with Four -dimensional reference points. Considering the influence of different learning processes and corresponding features on decision-making, a new reference-learning behavior is added, and a risk-based multiple-attribute decision-making method based on four-dimensional reference point cumulative prospect theory is proposed. Firstly, according to the cumulative prospect theory, the prospect value and the decision function value of the four reference points of learning, time, evaluation value and expected value are calculated respectively, and the cumulative prospect value matrix of each program dynamic is formed. Secondly,according to the WAA operalor, Maximize the stage weighting model to obtain the integrated cumulative prospect value. Finally, on the basis of this, the alternatives are sorted according to the size of the total cumulative prospect value, and compared with other methods, the validity and scientific of the proposed method are proved.

Impact of Capacity Parameters on Flexible Inventory Control Decision Model

From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to actively respond to demand fluctuations when there is a demand forecast error or a missing part of the demand information,and to avoid the risk of passive variable demand forecasting to set the immutable inventory capacity.At the same time,the game is controlled by the flexible and variable inventory control strategy and the customer’s willingness to demand.The paper mainly studies the influence of the setting of capacity parameters on the booking-limit decision and its benefits under the control of flexible space with variable total capacity.Through the two trends of capacity increase flexibility and capacity reduction flexibility in the flexible inventory control model,the mathematical performance and marginal utility methods are introduced to change the performance of the booking-limit control decision model under different scenarios.The correlation analysis between the capacity limit level and the return under the optimal Bookinglimit decision,and the above two flexibility parameters are obtained.

Diverse Voices and Democratic Policymaking： What Causes Japan＇s Nuclear Phase-Out Plan to Fail？

The nuclear disaster at Fukushima has raised questions about the nature of democratic policymaking in Japan. Focusing on nuclear policymaking post-Fukushima, this paper considers ways in which the diversity of public opinion can be connected with legislative processes. The disaster forced the government to rethink existing nuclear policies. To infuse public voices into government policy, the Democratic Party of Japan （DPJ） government introduced a new method of public participation called ＂national debates＂, comprising a series of public hearings, mass public comments, and deliberative polling. The government also attached importance to anti-nuclear rallies and opinion polls conducted by the media. The national debates highlighted that the majority of Japanese people supported a nuclear-free society; as a result, the government drafted a new energy plan to phase out nuclear power by 2039. Owing to both domestic and international opposition, the plan failed to become law. Progress was also impeded by the public＇s contradictory demands. However, this paper posits that inefficient legislative deliberation was the main factor behind the policy failure. The Japanese case suggests that effective legislative deliberation is crucial to assessing and coordinating numerous divergent public voices.

Earth Observation as a Support to Improve Water Use in Irrigated Agriculture

By the end of sixties, several satellites were launched to observe the earth; among them the Landsat missions have been important to study natural resources, especially in agriculture. Currently, Landsat 7, with some problems, and Landsat 5 are useful to evaluate crop development. A project called ＂Participatory multi-Level EO-assisted tools for Irrigation water management and Agricultural Decision-Support＂ （PLEIADES） was financed by the European Commission. This project considers eleven countries and 23 research institutions, Mexico being one of the selected countries. Landsat images are a basic tool of this project. Some of the results obtained in this study are presented in this article. By using w^getation indices obtained by the combinations of reflectances in different bands of the electromagnetic spectrum, it has been possible to estimate the behavior of several biophysical parameters of crops which are used to evaluate plant development, water stress, spatial soil variation, and the effect of plagues and crop diseases. To facilitate this process, a visor was developed, named SPIDER （System of Participatory Information Decision support and Expert knowledge for River basin management） whose use in this work is explained.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems

Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.

ANALYSIS OF BREAST CANCER PROFILES USING BAYESIAN NETWORK MODELING

Breast cancer is the leading cause of cancer-related death for women in Tunisia and the prognosis of its metastasis remains a major problem for oncologists despite advances in treatment. In this work we use Bayesian networks to develop a decision support system that is based on the modeling of relationships between key signaling proteins and clinical and pathological characteristics of breast tumors and patients. Motivated by the lack of prior information on the parameters of the problem, we use the Implicit inference for the structure and parameter learning. A dataset of 84 Tunisian breast cancer patients was used and new prognosis factors were identified. The system predicts a metastasis risk for different patients by computing a score that is the joint probability of the Bayesian network using parameters estimated on the learning database. Based on the results of the developed system we identified that overexpression of ErbB2, ErbB3, bcl2 as well as of oestrogen and progesterone receptors associated with a low level of ErbB4 was the predominant profile associated with high risk of metastasis.