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This study presents a comparison of two developed intelligent systems that carries out, in an integrated manner, failure diagnosis on electric power distribution feeders. These procedures aim to identify and classify critical situations, as high-impedance faults, which can potentially damage the system components and cause power supply interruptions to consumers. The intelligent systems combine the...
In different conditions such as light and complex backgrounds, we get some car images, the traditional methods are slow convergence speed and low accuracy. This paper presents a method which applies fuzzy theory to enhance several features of for target. To obtain the license information, we use an improved BP neural network algorithm, by through setting proper numbers of hidden layer of BP network,...
This paper proposes a composite method for short-term load forecasting, which is based on fuzzy clustering wavelet decomposition and BP neural network. Firstly, the similar-day's load is selected as the input load based on the fuzzy clustering method; secondly, the wavelet method is applied to decompose the similar-day load into the low frequency and high frequency components, from which the feature...
A novel model of fuzzy clustering neural network is discussed, which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model, an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature space. The experimental results obtained demonstrate that the algorithm is feasible and efficient.
Accurate fingerprint recognition system is the problem encountered in the field of biometric identification. Use of back propagation network for recognition problems resulted in inconsistent and unpredictable performance. The proposed approach using soft computing tools like Fuzzy, Neural and Genetic overcomes low recognition rate, low accuracy and increased time of recovery. The stages of this approach...
In this article, we have proposed an interactive image retrieval scheme using MPEG-7 visual features, Neural Network (NN)-based pre-classifier and fuzzy based feature evaluation scheme. The performance of the existing image retrieval systems is generally limited due to semantic gap, resulted due to the discrepancies between the computed low-level features and user's conception of an image. Partitioning...
This paper deals with a new approach for complex systems modeling and control based on neural and fuzzy clustering algorithms. It aims to derive a base of local models describing the system in the whole operating domain. The implementation of this approach requires three main steps: 1) determination of the structure of the model-base, the number of models are found out by using Rival Penalized Competitive...
Based on multiple input single output of adaptive fuzzy neural network, this paper design the integrated adaptive fuzzy neural network based on the Takagi-Sugeno type fuzzy rules, adopt a hybrid learning algorithm to train the network connection weights, optimize membership function. Simulation results verified the effectiveness and feasibility of this method.
In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor,...
This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All...
This paper innovatively proposes a hybrid intelligent system combining fuzzy comprehensive assessment approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. And also inducts the sensibility analysis to discriminate the importance of each index in the assessment index system. The effectiveness...
According to the Characteristics of complex object system, a comprehensive evaluation model for complex object system is established based on fuzzy theory and artificial neural network. To realize the intelligence and the visualization of the evaluation process, an intelligent comprehensive evaluation software system with the help of Visual Basic, database technique and MATLAB toolbox is designed...
According to the complexity of collaborative medical diagnosis system, a model based on C-type neural is given. Based on the relation between C-type neural and fuzzy cognitive-map (FCM), how to compose many cognitive-maps(CM) and derive new CM is described. Because the system can integrate more knowledge from each agent both effect and side effect, the diagnosis error is minimized.
The goal of this research is to identify the significant factors affecting the firm performance and estimate the system behavior in different operating conditions. By determining the statistical relations of the productivity and effectiveness of the firm with these factors, a decision-making framework can be provided to improve the system performance within the competitive strategy of the whole supply...
This paper presents a novel method to odor based identification of alcoholic beverages using steady-state responses of a thick film tin oxide sensor array exposed to four different types of whiskies. A neural classifier designed to perform the identification task was trained by incorporating the class information in the training data set in the form of fuzzy entropies of the respective classes. The...
A novel ensemble neural network structure is presented for automatic classification of power quality disturbances. Power quality (PQ) disturbances analysis is the focus of power quality control. The characteristics of PQ disturbances include short duration, variety of types and so on. Power quality disturbances classification is the foundation of power quality control automation. Different types of...
The advent of data mining has contributed significantly to the field of customer management. The payment center in Maersk finds that it's difficult to charge customers for the balance due. Thus the research on the application of artificial neural networks in classification of data mining will be conducted in this paper. Since the knowledge captured by neural networks is represented at a sub-symbolic...
Information security risk assessment is essential to government for making an efficient and effective security management plan. This paper firstly established a hierarchy structure index system for E-government information systems security risk assessment based on the operationally critical threat, assets and vulnerability evaluation (OCTAVE). Considering that the previous weights selection methods...
Due to high penetration of wind generation in modern power systems, the influence of wind power production over the efficient operation of the power system is increasingly complex. Hence, an increasing interest is shown by different actors in the wind energy market to develop and enhance existent forecasting methods for power generated by wind farms. This paper presents the experience with wind power...
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