Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Classification is one of the task involved in data stream mining that maps data into predefined groups or class...
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ISBN:
(纸本)9788132204909
Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Classification is one of the task involved in data stream mining that maps data into predefined groups or classes. Most of the stream learning algorithms learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. Built model will always have high accuracy on the training data, but performance on unseen data is to be checked. Performance of different classifiers for same task in same environment can differ, so there is need for some method which will help one to select the best suited classifier for the required task. Performance comparison will be effective if graphical interface facility is given. There is a need of user friendly interface having facility of multiple classifier selection for performance comparison, saving environment for future use and plotting the performance graph of classifiers. A framework which will provide different measures for performance comparison like true positive rate, true negative rate etc. is today's requirement. Objective of this paper is to enhance the existing software used for stream data analysis with the above mentioned facilities.
Combined CMAC addressing schemes with fuzzy logic idea, a general fuzzified CMAC (GFAC) is proposed, in which the fuzzy membership functions are utilized as the receptive field functions. The mapping of receptive fiel...
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ISBN:
(纸本)9783642279447
Combined CMAC addressing schemes with fuzzy logic idea, a general fuzzified CMAC (GFAC) is proposed, in which the fuzzy membership functions are utilized as the receptive field functions. The mapping of receptive field functions, the selection law of membership with its parameters and the learning algorithm are presented. By using GFAC, the approximation of complex functions can be obtained which is more continuous than using conventional CMAC. The simulation results show that GFAC has good generalization, proper approximate accuracy and capacity to calculate function differential output.
In order to solve function approximation, a mathematic model of Rational Function Functional Networks (RFFN) based on approximation was proposed and the learning algorithm for function approximation was presented. Thi...
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ISBN:
(纸本)9783037855034
In order to solve function approximation, a mathematic model of Rational Function Functional Networks (RFFN) based on approximation was proposed and the learning algorithm for function approximation was presented. This algorithm used the lease square method thought and constructed auxiliary function by Lagrange multiplier method, and the parameters of the rational function functional networks were determined by solving a system of linear equations. Results illustrate the effectiveness of the rational function functional networks in solving approximation problems of the function with a pole.
The quantron is a hybrid neuron model related to perceptrons and spiking neurons. The activation of the quantron is determined by the maximum of a sum of input signals, which is difficult to use in classical learning ...
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The quantron is a hybrid neuron model related to perceptrons and spiking neurons. The activation of the quantron is determined by the maximum of a sum of input signals, which is difficult to use in classical learning algorithms. Thus, training the quantron to solve classification problems requires heuristic methods such as direct search. In this paper, we present an approximation of the quantron trainable by gradient search. We show this approximation improves the classification performance of direct search solutions. We also compare the quantron and the perceptron's performance in solving the IRIS classification problem.
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical ...
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ISBN:
(纸本)9781467324212
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for I-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.
Neuro-fuzzy systems have been proposed for different applications for many years. In this paper, a neuro-fuzzy serial-propagated multi-step-ahead predictor is developed for time series prediction. The predictor consis...
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ISBN:
(纸本)9781467327435;9781467327428
Neuro-fuzzy systems have been proposed for different applications for many years. In this paper, a neuro-fuzzy serial-propagated multi-step-ahead predictor is developed for time series prediction. The predictor consists of several individual neuro-fuzzy networks to produce a series of predicted values. Each network is trained by a hybrid learning algorithm. Two benchmark data sets are used to demonstrate the effectiveness of the proposed serial-propagated architecture. Experimental results show that our approach can provide more accurate predictions than other traditional methods.
The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for ...
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ISBN:
(纸本)9781467307840
The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for improving the efficiency of such system. The aim of this approach is to classify the objectives for a real-world case study which presents a major problem for controlling the quality levels of its production lines. This approach provided a significant improvement when the testing data are various or complex.
This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studie...
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ISBN:
(纸本)9780878492060
This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.
This paper proposes a Fuzzy Inference Net (FIN) method for electricity price zone forecasting. Under smart grid environment, it is important for players to maximize profits and minimize risks though power markets whil...
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This paper proposes a Fuzzy Inference Net (FIN) method for electricity price zone forecasting. Under smart grid environment, it is important for players to maximize profits and minimize risks though power markets while introducing renewable energy into grids. The time series of electricity price becomes more complicated due to the nonlinearity and uncertainties. To capture the behavior of the time series appropriately, more sophisticated methods are required to overcome them as a prediction tool. In this paper, a new method is proposed for price zone forecasting. The proposed method makes use of FIN that evaluates the association probability of unknown data to predetermined clusters with fuzzy inference and self-organization. The selection of input variables is determined by the variable importance of the CART algorithm of data mining. The association probability is used determine which zone the one-step ahead electricity price belong to. The proposed method is tested for real data in comparison with the conventional artificial neural network.
In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evalua...
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In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.
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