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In power distribution fault data, the percentage of faults with different causes could be very different and varies from region to region. This data imbalance issue seriously affects the performance evaluation of fault diagnosis algorithms. Due to the limitations of conventional accuracy (ACC) and geometric mean (G-mean) measures, this paper discusses the application of Receiver Operating Characteristic...
Most methods constituting the soft computing concept can not handle data with missing or unknown feature values. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. In the paper we incorporate rough set theory to neuro-fuzzy system of very specific type. This results in learning systems which can work when the set number of available feature values...
A system based on Artificial Neural Network (ANN) for load predication in power distribution system with chillers is presented in this paper. The proposed system could predict the chillers' load each hour in a day by using the weather conditions as input. Results from this system provide possibility to improve the power factor of the chillers system and to optimize the power distribution in loads...
This paper presents the development and commercialisation of a computerised, non-invasive psychological profiling system (named `Silent Talker'), for the analysis of nonverbal behaviour. Nonverbal signals hold rich information about mental, behavioural and/or physical states the detection of which would be beneficial in a wide variety of commercial domains, particularly deception detection and security...
New product development (NPD) is considered to be one of the best strategies to handle the rapid changes that occur in the market for a product, in order to sustain competitive position. Though some researchers attempt to study factors that affect the capability of NPD by a firm; none of them tried to quantify them. Quantification of the factors is essential in order to achieve adequate control over...
A quantum neural network classifier is presented, which can classify chinese Web information into each subject. Based on this quantum neural network classifier, a framework of chinese Web information navigation is proposed. We choose keywords of Web document as inputs of quantum neural network classifier, and choose subjects code as outputs of quantum neural network classifier. In order to evaluate...
Characteristics in EEG signals related to the motor imagery can be used to build up a biometric system. However, for the practical implementation of a biometric system, the classifier plays a crucial role. In this paper, I compared the performance of three different classifiers for the detection of the imagined movements in a group of subjects on the basis of EEG signals. The classifiers compared...
Focusing on the problem in production practice of sintering process, a novel classifier based on BP learning algorithm is proposed for on-line quality inference of sintered ore. In order to speed up the convergence rate of BP learning algorithm, the learning algorithm with adaptive variable step-size is adopted. On the basis of the above work a quality prediction model is proposed in this paper. Experimental...
Internet Automatic Sales System is be used to assist people in net business. Since part of the talking continent in purchasing is always asked frequently, so the statistic method like TFIDF is applied to deal this issue. Based on the experiment situation, baseline system based on TFIDF achieved very good effect. But the classic algorithm is not enough to reach real application requirement, the Dice...
General regression neural networks (GRNN) has a strong ability of approaching non-linear function. It can find the hidden relation between independent variables with dependent variables according to the training sample data. The optimization of the smoothing parameters is crucial to the performance of GRNN, and it is also the essence and difficulty of GRNN training. A modified genetic algorithms (MGA)...
The Gamma Test has attracted the attention of many researchers in the nonlinear modeling field, especially with Artificial Neural Networks. In theory, the test should provide a modeler with valuable information to find the best input variables without extensive model development for each potential input combination. However, it has been found that the Gamma Test does not always point to the best input...
We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving...
This paper presents an approach for terrain identification in grayscale images based on recurrent neural networks. The network in this work has 16 inputs that represent 16, horizontally contiguous pixels from the grayscale image. The network is trained as a binary classifier that classifies the input pixels while being scanned from the top to the bottom of the image. Experiments were performed on...
In this paper a new algorithm is proposed for Short Term Load Forecasting (STLF) using Echo State Networks (ESN). Hourly load data along with only average temperature of each day and day type flag is fed to the ESN and nonlinear mapping is done using training methods. Despite conventional recurrent neural networks, ESN can be trained much easier and with great deal of accuracy. Simulation results...
The maze traversal problem involves finding the shortest distance to the goal from any position in a maze. Such maze solving problems have been an interesting challenge in computational intelligence. Previous work has shown that grid-to-grid neural networks such as the cellular simultaneous recurrent neural network (CSRN) can effectively solve simple maze traversing problems better than other iterative...
This paper presents a method to improve generalization capabilities of supervised neural networks based on topological data mapping used in Counter Propagation Networks (CPNs). Using topological data mapping on CPNs the method presented herein provides advantages to interpolate new data in sparse areas that exist among categories and to remove overlapping or conflicting data in original training data...
When training a neural network it is tempting to experiment with architectures until a low total error is achieved. The danger in doing so is the creation of a network that loses generality by over-learning the training data; lower total error does not necessarily translate into a low total error in validation. The resulting network may keenly detect the samples used to train it, without being able...
The third component's ingredient which is between the Pb and the Al and the hot dipping temperature has the major impact on the physical property of the Pb-Al composite. Therefore if needs to change the ingredient of the third component and the hot dipping temperature in the preparation process, then measure the related indicators of its physical proper to find out the optimum combination. It is complex...
Neural network based models are developed and used for the description of the relations of the geometry characteristics of Steel 3 welds from orbital arc welding (OAW) process parameters. This integrated methodology is implemented together with response surface methodology (statistical approach) for the investigation of the defined as quality characteristics: outer and inner weld widths. Both implemented...
This paper presents the artificial neural network (ANN) that used to perform the short-term load forecasting (STLF). The input data of ANN is comprises of multiple lags of hourly peak load. Hence, imperative information regarding to the movement patterns of a time series can be obtained based on the multiple time lags of chronological hourly peak load. This may assist towards the improvement of ANN...
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