Granular neural web agents for stock prediction

 A granular neural Web-based stock prediction agent is developed using the granular neural network (GNN) that can discover fuzzy rules. Stock data sets are downloaded from www.yahoo.com website. These data sets are inserted into the database tables using a java program. Then, the GNN is trained using sample data for any stock. After learning from the past stock data, the GNN is able to use discover fuzzy rules to make future predictions. After doing simulations with six different stocks (msft, orcl, dow, csco, ibm, km), it is conclusive that the granular neural stock prediction agent is giving less average errors with large amount of past training data and high average errors in case of fewer amounts of past training data. Java Servlets, Java Script and jdbc are used. SQL is used as the back-end database. The performance of the GNN algorithm is compared with the performance of the BP algorithm by training the same set of data and predicting the future stock values. The average error of the GNN is less than that of BP algorithm.     

Granular neural networks for land use classification

Granulation of information is a new way to describe the increased complexity of natural phenomena. The lack of clear borders in nature calls for a more efficient way to process such data. Land use both in general but also as perceived in satellite images is a typical example of data that are inherently not clearly delimited. A granular neural network (GNN) approach is used here to facilitate land use classification. The GNN model used combines membership functions of spectral as well as non-spectral spatial information to produce land use categories. Spectral information refers to IRS satellite image bands and non-spectral data are here of topographic nature, namely slope, aspect and elevation. The processing is done through a standard neural network trained by back-propagation learning algorithm. A thorough presentation of the results is given in order to evaluate the merits of this method.     

Granular neural networks for numerical-linguistic data fusion andknowledge discovery

We present a neural-networks-based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include 1) how to make neural networks process both numerical and linguistic data in a database, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to use neural networks to do numerical-linguistic data fusion, 4) how to use neural networks to discover granular knowledge from numerical-linguistic databases, and 5) how to use discovered granular knowledge to predict missing data. In order to answer the above concerns, a granular neural network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database. The GNN is also capable of greatly compressing low-level granular data to high-level granular knowledge with some compression error and a data compression rate. To do KDDM in huge databases, parallel GNN and distributed GNN will be investigated in the future.     

粒度神经网络对数字语言的数据融合

1 引言知识发现和数据挖掘(Knowledge discoverg and dataMining,简称KDDM)是近几年来随着人工智能和数据库发展起来的一门新兴的数据库技术。其处理对象是海量的日常业务数据,其目的是从大量的数据源中提取人们感兴趣的、有价值的知识和重要的信息。由于计算机和通信技术的迅猛发展,人类活动产生的数据日益增加,大量的各种数据库用于政府事务、科学研究、工业生产、商业管理和其它各个方面。数据的爆炸式增长使KDDM成了一个日益重要的研究领域。所提取的知识可用于问题求解、生产控制、信息管理、判断决策     

Constructive granular systems with universal approximation and fast knowledge discovery

Conventional gradient descent learning algorithms for soft computing systems have the learning speed bottleneck problem and the local minima problem. To effectively solve the two problems, the n-variable constructive granular system with high-speed granular constructive learning is proposed based on granular computing and soft computing, and proved to be a universal approximator. The fast granular constructive learning algorithm can highly speed up granular knowledge discovery by directly calculating all parameters of the n-variable constructive granular system using training data, and then construct the n-variable constructive granular system with any required accuracy using a small number of granular rules. Predictive granular knowledge discovery simulation results indicate that the direct-calculation-based granular constructive algorithm is better than the conventional gradient descent learning algorithm in terms of learning speed, learning error, and prediction error.     

A framework for rule-base evidential reasoning in the interval setting applied to diagnosing type 2 diabetes

A new framework for rule-base evidential reasoning in the interval setting is presented. While developing this framework, two collateral problems such as combining and normalizing interval-valued belief structures from different sources and comparing resulting belief intervals, the bounds of which are intervals, arise. The first problem is solved with the use of the so-called “interval extended zero” method. It is shown that interval valued results of the proposed approach to combining and normalizing interval-valued belief structures are enclosed in those obtained by known methods and possess three desirable intuitively obvious properties of normalization procedure defined in the paper. The second problem is solved using the method for interval comparison based on the Demposter-Shafer theory providing the interval valued results of comparison. The advantages of the proposed framework for rule-base evidential reasoning in the interval setting are demonstrated using the developed expert system for diagnosing type 2 diabetes.     

Polynomial regression interval-valued fuzzy systems

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system (FLS). In order to make the type-2 FLS reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and probability type reduce reasoning method for the interval-valued FLS are developed. We have implemented the proposed non-linear (polynomial regression) statistical interval-valued type-2 FLS to perform smart washing machine control. The results show that our quadratic statistical method is more robust to design a reliable type-2 FLS and also can be extend to polynomial model.     

用神经网络驱动的模糊推理入侵检测方法

提出了神经网络驱动模糊推理的入侵检测方法,利用神经网络的学习能力,对不清楚规则的复杂系统的输入输出特性进行适当的非线性划分,自动形成舰则集和相应的隶属关系,克服了在多维空间上经验性的确定隶属函数的困难。对于神经网络的训练数据,采用人工数据,克服了神经网络监督学习和获取已知输出的训练数据的困难。试验证明,这种技术具有很好的灵敏度和鲁棒性,而且,能够检测出未知的入侵行为。     

Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.     

基于OWA算子的区间值加权模糊推理

针对如何对区间值模糊产生式规则赋予合理权值的问题,将OWA算子引入到区间值模糊推理中。介绍一种基于OWA算子的区间值赋权方法,根据此方法给出区间值模糊集上的加权模糊产生式规则的推理算法。在采用该算法的过程中,为合理地计算输入事实与规则前件的匹配程度,引入基于OWA算子的区间值模糊匹配函数值和总体贴近度的计算方法。实例分析表明了所给出的区间值模糊推理算法的有效性和可行性。     

一种基于Vague集相似度量推理的控制器设计

针对模糊变量隶属值难以确定、描述信息单一这一问题,将区间值模糊集引入控制领域。在Vague集相似度量推理基础上,设计了一种模糊控制器。为了应用Vague集相似度量推理方法,给出了一种清晰量的区间值模糊化方法。说明了该类模糊控制器设计过程,给出Matlab仿真控制效果。仿真结果表明,该控制器隶属函数值较易确定,设计过程简化,模糊化过程偏差小,有很好的应用价值。     

Development of an interval-valued fuzzy linear-programming method based on infinite α-cuts for water resources management

An interval-valued fuzzy linear-programming (IVFL) method based on infinite α-cuts is developed for water resources management in this study. The introduction of interval parameters and interval-valued fuzzy parameters into the objective function and constraints makes it possible for dealing with individual uncertainty and dual uncertainties existing in many real-world cases. A two-step infinite α-cuts (TSI) solution method is communicated to the solution process to discretize infinite α-cuts to interval-valued fuzzy membership functions. Application to an agricultural irrigation problem indicates that interval-valued fuzzy sets can represent dual uncertainties in modeling parameters, and the solution method is able to generate decisions with enhanced reliability. It is also indicated that the objective (i.e. system net benefit) can be increased with the growth of violation risk, in association with a set of different allocation schemes. As the key segment of interval-valued fuzzy membership functions that could significantly affect system performance can be identified through the analysis of decision alternatives under different risk levels of constraint violation, the IVFL method provides decision makers flexibility in selecting an appropriate decision scheme according to their preference and practical conditions.     

区间值加权模糊推理方法

提出了一种新的带权的区间值模糊产生式规则,给出了加权模糊匹配函数和一个区间值排序算法。在此基础上给出了区间值加权模糊推理方法。该方法使规则前件与事实的匹配更符合实际情况,得到的结果更便于实际应用,并且解决了文献犤1,2犦中模糊推理方法存在的不足。     

POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier

A pseudo-outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier [POPFNN-CRI(S)] is proposed in this paper. The correspondence of each layer in the proposed POPFNN-CRI(S) to the compositional rule of inference using standard T-norm and fuzzy relation gives it a strong theoretical foundation. The proposed POPFNN-CRI(S) training consists of two phases; namely: the fuzzy membership derivation phase using the novel fuzzy Kohonen partition (FKP) and pseudo Kohonen partition (PFKP) algorithms, and the rule identification phase using the novel one-pass POP learning algorithm. The proposed two-phase learning process effectively constructs the membership functions and identifies the fuzzy rules. Extensive experimental results based on the classification performance of the POPFNN-CRI(S) using the Anderson's Iris data are presented for discussion. Results show that the POPFNN-CRI(S) has taken only 15 training iterations and misclassify only three out of all the 150 patterns in the Anderson's Iris data.     

基于VISIT算法设计模糊分类器的进化方法

介绍了在没有数据分布先验知识的情况下,用进化方法直接从训练数据中建立紧致模糊分类系统的方法。使用VISIT算法获取每个个体模糊系统,再用遗传算法从中搜索最优的模糊系统。规则和隶属函数是在进化过程中自动建立和优化的。为了同时有效地评价系统的精度和紧致性,用一个模糊专家系统作适应度函数。在2个基准分类问题上的实验结果表明了新方法的有效性。     

序区间值决策系统中最优可信规则获取与约简

基于描述子的规则获取可导出序值决策系统中的所有可信规则,但对包含区间值序决策系统却不能有效支持。因此,首先根据每个属性值域的范围,提出了一个区间段值的概念,用以将序区间值决策系统转换为序区间段值决策系统;然后,在序区间段值决策系统中提出了基于区间段值的优势和弱势描述子概念,用以导出序区间值决策系统中的所有可信规则;最后,研究了两种新的描述子的约简以及相对约简问题,给出了相应的判定定理与区分函数。以上为从序区间值决策系统中获取有效的最优可信决策规则提供了一种新理论基础与操作手段。     

Some interval-valued q-rung orthopair weighted averaging operators and their applications to multiple-attribute decision making

Q-rung orthopair fuzzy sets (q-ROFSs), initially proposed by Yager, are a new way to reflect uncertain information. The existing intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets are special cases of the q-ROFSs. However, due to insufficiency in available information, it is difficult for decision makers to exactly express the membership and nonmembership degrees by crisp numbers, and interval membership degree and interval nonmembership degree are good choices. In this paper, we propose the concept of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) based on the ideas of q-ROFSs and some operational laws of q-rung orthopair fuzzy numbers (q-ROFNs). Then, some interval-valued q-rung orthopair weighted averaging operators are presented based on the given operational laws of q-ROFNs. Further, based on these operators, we develop a novel approach to solve multiple-attribute decision making (MADM) problems under interval-valued q-rung orthopair fuzzy environment. Finally, a numerical example is provided to illustrate the application of the proposed method, and the sensitivity analysis is further carried out for the parameters.     

Fuzzy rule interpolation based on principle membership functions and uncertainty grade functions of interval type-2 fuzzy sets

Fuzzy rule interpolation is an important research topic in sparse fuzzy rule-based systems. In this paper, we present a new method for dealing with fuzzy rule interpolation in sparse fuzzy rule-based systems based on the principle membership functions and uncertainty grade functions of interval type-2 fuzzy sets. The proposed method deals with fuzzy rule interpolation based on the principle membership functions and the uncertainty grade functions of interval type-2 fuzzy sets. It can deal with fuzzy rule interpolation with polygonal interval type-2 fuzzy sets and can handle fuzzy rule interpolation with multiple antecedent variables. We also use some examples to compare the fuzzy interpolative reasoning results of the proposed method with the ones of an existing method. The experimental result shows that the proposed method gets more reasonable results than the existing method for fuzzy rule interpolation based on interval type-2 fuzzy sets.     

Evolutionary design of a fuzzy classifier from data

Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.     

大规模图神经网络系统综述

图神经网络(GNN)是一类基于深度学习的处理图域信息的方法,它通过将图广播操作和深度学习算法结合,可以让图的结构信息和顶点属性信息都参与到学习中,在顶点分类、图分类、链接预测等应用中表现出良好的效果和可解释性,已成为一种广泛应用的图分析方法.然而现有主流的深度学习框架(如TensorFlow、PyTorch等)没有为图...