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This paper introduces a method for the fault diagnosis of a rotor system. For a vibration signal of a rotor system fault, an AR model is established first, and then the related parameter and amplitude spectrum of this mode can be obtained, etc. The experiments show the above-mentioned method can effectively diagnose the fault of a rotor system.
Long range dependence is closely linked with self-similar stochastic processes and random fractals, which have been considered extensively for signal processing applications and computer network traffic modeling. The Hurst parameter captures the amount of long-range dependence in a time series. Typically, the analysis of self-similar series is performed using: the variance-time plot, the R/S plot,...
In order to describe the camera internal geometrical and optical parameters, and the relation between camera and object in three-dimensional space, traditional camera calibration methods usually need to assume lots of priori knowledge, establish precise mathematical model and then decompose and calculate those parameters we need. While artificial neural network has outstanding non-linearity mapping...
In video communication, packet is inevitably lost or transmitted erroneously over error-prone channel. If there are packets lost, the entire video quality will be degraded. The error concealment is thus proposed to solve this problem effectively. Therefore, this paper will propose the general regression neural network (GRNN) can be used to estimate the motion vectors of the corrupted macroblocks....
The cost of experimental setup during an assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under fill process consists of only a few samples. As a result, existing machine learning algorithms for predictive modeling cannot be applied to this setting. Despite...
Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction...
This study using computer image processing and artificial neural network sensor technologies constructs a method of identifying ice slurry density based on the value of ice color image. The method is applied to the Jinan section of the Yellow River through the ice image acquisition, R/G color extraction, network learning and training, the final output target value of ice or water, and the actual image...
This paper presents the retro-propagation algorithm for tuning the parameter of Artificial Neural Networks used by pharmachemical industry. The numerical test results obtained on lubrication and air circuits shown that the proposal improve the performance in terms of number of iterations and reliability of the models. BEKER Laboratories production line, is a Pharmaceutical production company located...
This paper uses generalized congruence function instead of transfer function of classical BP neural network, and improve convergence rate of neural network. We introduce the subsection generalized derivation, error back propagation derivation mechanism of classical BP algorithm to adjust weight vector in generalized congruence neural network, and modify generalized congruence neural network, and then...
After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using Principle Component Analysis and recognition using the feed forward back propagation...
Local minimum is incorporated problem in neural network (NN) training. To alleviate this problem, a modification of standard backpropagation (BP) algorithm, called BPCL for training NN is proposed. When local minimum arrives in the training, the weights of NN become idle. If the chaotic variation of learning rate (LR) is included during training, the weight update may be accelerated in the local minimum...
Radial basis function neural networks (RBFNN) are used to dynamically identify harmonics content in converter waveforms based on p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the harmonic contents are identified over a wide operating range. The proposed RBFNN filtering training algorithm are based on systematic and computationally efficient training method called...
Texture classification is an important and challenging factor in image processing system which refers to the process of partitioning a digital image into multiple constituent segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Artificial Neural Network (ANN) Based texture classification or Segmentation...
Adaptive Neuro-Fuzzy Inference System (ANFIS) has become a popular tool in neuro-fuzzy modeling. However, since it includes many parameters needed to be set, its designing process is a complicated and time-intensive task for experimenters. To tackle this problem, in this paper we implement the Design of Experiment (DOE) technique to identify the significant parameters of ANFIS when it applies to the...
An important issue in design and implementation a neural network is that perturbations of training pattern pairs may cause some disadvantages to outputs. How the perturbations of training pattern pairs in Morphological Bidirectional Associative Memories (MBAMs) influence on the outputs is discussed in this paper. We define the outputs' max error to evaluate the robustness of the MBAMs. The related...
The paper first makes a thorough research on the method for analog circuit fault diagnosis based on kurtosis and negentropy, and then theoretically analyses it's advantage and disadvantage, which is followed by introducing the idea of centroid to overcome the method's shortcoming, making the improved method can extract the signal's feature more efficiency. Finally, it applies the improved method to...
Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
Vibration faults frequently occurring to the feed water pump in large-scale power plants are diagnosed by the integrated neural network based on MATLAB. The integrated neural network for fault diagnosis is established from individual neural network and on the basis of information fusion. The strategies and principles for the realization and formation of integrated neural network are analyzed and a...
Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional...
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