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This paper attempts to put forward a hybrid model which combines the advantages offered by grey systems theory, fuzzy theory and neural networks. While φ -fuzzy sub-set offers the suitable tools for the treatment of uncertainty and subjectivity, grey systems theory is used for variables selection. Also, neural networks pattern recognition facility is used in order to determine each company's bankruptcy...
In this paper, we propose a new method for enrollments prediction, based on fuzzy time series. The new method constructs high-order fuzzy logical relationships with high-order heuristic function based on the historical data and uses nature-ratio techniques to partition the length of each interval in the universe of discourse for enrollments forecasting to increase the prediction accuracy rate. The...
The inhabited environments are MIMO, uncertainly, and nonlinear complex systems. This paper presents a novel intelligent fuzzy agent(IFA) based on input-output associational algorithm for intelligent inhabited environments. An input-output dynamic associational algorithm based on Hebb learning is proposed, which can divide a complex system into multiple simple systems and eliminate the irrelevant...
According to the thought of intelligent forecasting and hybrid forecasting, an Intelligent Hybrid (IH) model for short-term traffic flow forecasting was presented. The IH model had three sub-models: History Mean (HM) model, Artificial Neural Network (ANN) model and the Fuzzy Combination (FC) model. By means of the good static stabilization character of HM method, the HM model predicted the traffic...
The convergence theory is a basic theory of fuzzy mathematics and have very important applications in economics, management science, information technology and computer science. In this paper we introduce the notions of fuzzy PS-upper limit, fuzzy PS-lower limit and the fuzzy PS-convergent nets of fuzzy sets. We also study the properties of fuzzy PS-upper limit, fuzzy PS-lower limit and the fuzzy...
A novel algorithm of Electromagnetic Environment (EME) is proposed in this paper. Assessment indices are established based on analysis of factors influenced electromagnetic environment complex. Index weights are acquired by fuzzy comprehensive evaluation method. The grade of complexity is computed based on complex evaluation values according to the grade standard of complexity. Experimental result...
QoS (Quality of service) of railway passenger transportation is very important. Based on the thinking of passenger service, its evaluation indexes (or factors) are classified and disassembled into the multi-level structures, and form the hierarchal evaluating models. This paper discusses the deficiencies in 1-9 scaling method, and the frequency analysis method is introduced to compute the weight matrix...
In recent years, the exposure of the mansions of Silver, Enron and other major financial scandals led an unprecedented credit crisis and moral crisis to the audit, and they also raises people's researching interest in the quality of audit and its impact factors. At present, domestic and foreign scholars have done lots of work on the factors affecting the quality of audit, but the literature is mostly...
Data envelopment analysis (DEA) is a non-parametric technique based on linear programming in which multiple inputs and outputs are simultaneously used in order to measure technical efficiency. All of the research efforts have used DEA approach as a tool for evaluating what has been occurred up to the present time. However, due to the time lag, it is usually too late for the Decision Making Units (DMUs)...
We propose a cost estimation model based on a fuzzy rule back-propagation network (BPN), configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach we determine the optimal path in the manufacturing network. Finally, an application...
The presentation is focused on comparison of neural networks and fuzzy systems. Advantages and disadvantages of both technologies are discussed. Fuzzy systems are relatively easy to design but number of inputs in the system are significantly limited. It is very difficult to design neural networks so rather they have to be trained instead. Neural networks produce much smoother nonlinear mapping than...
Decision Theoretic Rough Set (DTRS) model provides a three-way decision approach to classification problems, which allows the classifier to make a deferment decision on the hard cases, rather than forced to make an immediate determination. The deferred cases must be reexamined by collecting further information. Although the formulation of DTRS is intuitively appealing, a fundamental question that...
Edge detection is a previous step for image recognition systems that helps to extract the most important shapes in an image, ignoring the homogeneous regions and remarking the real object to classify or recognize. Traditional and fuzzy edge detectors can be used, but it's very difficult to demonstrate which one is better before the recognition results are obtained. In this work we present an experiment...
Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method which is based on selective...
In this paper we develop a model which, through a dynamic modulatory local feedback mechanism, is capable of categorizing multiple simultaneously-presented input patterns while only previously being trained on single patterns. The key process is shown to be segmentation where low level feature detectors are grouped with their corresponding high level categories. While most existing approaches use...
Considering the instability of airport unexploded ordnances(UXO) and the complexity of environment, the theory of artificial neural network(ANN)-fuzzy support vector machines (FSVMs) is presented to penetrate UXO. Different from the traditional target identification methods, the proposed approach uses the characteristics of ground penetrating radar target data analyzed by using the principal component...
It is widely recognized that the human reasoning can be approximated by fuzzy rule-based (FRB) systems which can be seen as one of the basic frameworks for representation of intelligent systems. During the last quarter of a century two particular types of FRB systems, namely Zadeh-Mamdani (ZM) and Takagi-Sugeno (TS) dominated the field. In this paper we propose an alternative type which is simpler...
An ANN-based dynamic FQFD method is proposed in order to solve the dynamic and fuzzy nature of QFD, that solves the problem of the dynamic nature by using neural network method, while the introduction of trapezoidal fuzzy number for its ambiguity. Firstly, a combined method with neural networks and FQFD is established, after learning and training, the method can quickly and effectively deliver customer...
The single input rule modules connected fuzzy inference method (SIRMs method) can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional type single input rule modules connected fuzzy inference method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods...
Traditional hardware memory cannot generalize or learn from neighborhood information. Many software neural networks and fuzzy algorithms can solve above problems, but their processes are complicated. Therefore, a simple firmware memory combines learning ability with generalization ability, which applies the CMAC coding schemes on FPGA, is proposed in this paper. The firmware memory includes two parts:...
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