This paper proposes a new approach for training FNN by, hybrid DE and BP. It combines the advantages of the global search performed by DE over the FNN parameter space and the local search of BP. Using a function appro...
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ISBN:
(纸本)0387283188
This paper proposes a new approach for training FNN by, hybrid DE and BP. It combines the advantages of the global search performed by DE over the FNN parameter space and the local search of BP. Using a function approximation as an illustration, we compare the HDEBP and BP for effectiveness and efficiency for training FNN. It shows that the use of new method can provide better results than BP.
The runoff series always presents complex chaos phenomenon with higher embedded dimensions because of the influence of many complicated facts. Generally, it is an effective method to combine phase space restructures t...
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ISBN:
(纸本)0387283188
The runoff series always presents complex chaos phenomenon with higher embedded dimensions because of the influence of many complicated facts. Generally, it is an effective method to combine phase space restructures theory with artificialneuralnetworks (ANN) model for runoff forecasting. The traditional methods that not consider the variations of the character of full Lyapunov exponential spectrum in restructure space, are proved of high precision to forecast time series with low-embedded dimension. But, they are not so effective to forecast attractors with high-embedded dimension. The paper proposes a new method, which improves the character of full Lyapunov exponential spectrum in a restructure space with high dimensions. In the mean time, an artificialneuralnetworks model based on determinate mutation evolutionary programming (DMEP) learning algorithm is presented for chaotic runoff series forecasting. DMEP introduces chaos mapping into the mutation operation of EP, which aim to increase its convergence rate and results' precision. The test result of runoff forecasting series shows that the precision of runoff forecasting is improved by means of the new method when the embedded dimension is high.
In order for humans to interact with computers a fast and easy way is to use hand gestures. The aim of this project is the recognition of hand gestures using an inexpensive camera with fast computation time. For this ...
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Face recognition is one of the most important imageprocessing research topics which is widely used in personal identification, verification and security applications. In this paper, a face recognition system, based o...
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ISBN:
(纸本)3540262083
Face recognition is one of the most important imageprocessing research topics which is widely used in personal identification, verification and security applications. In this paper, a face recognition system, based on the principal component analysis (PCA) and the feedforward neural network is developed. The system consists of two phases which are the PCA preprocessing phase, and the neural network classification phase. PCA is applied to calculate the feature projection vector of a given face which is then used for face identification by the feedforward neural network. The proposed PCA and neural network based identification system provides improvement on the recognition rates, when compared with a face classifier based on the PCA and Euclidean Distance.
An intelligent decision support tool to the Radiologist in telemedicine is described. Medical prescriptions are given based on the images of cyst that has been transmitted over computer networks to the remote medical ...
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ISBN:
(纸本)0819456462
An intelligent decision support tool to the Radiologist in telemedicine is described. Medical prescriptions are given based on the images of cyst that has been transmitted over computer networks to the remote medical center. The digital image, acquired by sonography, is converted into an intensity image. This image is then subjected to image preprocessing which involves correction methods to eliminate specific artifacts. The image is resized into a 256 x 256 matrix by using bilinear interpolation method. The background area is detected using distinct block operation. The area of the cyst is calculated by removing the background area from the original image. Boundary enhancement and morphological operations are done to remove unrelated pixels. This gives us the cyst volume. This segmented image of the cyst is sent to the remote medical center for analysis by Knowledge based artificial Intelligent Decision Support System (KIDSS). The type of cyst is detected and reported to the control mechanism of KIDSS. Then the inference engine compares this with the knowledge base and gives appropriate medical prescriptions or treatment recommendations by applying reasoning mechanisms at the remote medical center.
Because sharply focused images inherently contain more information than defocused images, automatically obtaining the sharp image of a scene is an important task in computer vision. A camera can be sharply focused on ...
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Impulsive noise is a problem frequently occurred in imageprocessing. This problem gets an importance especially when the important details in images having highly intensive impulsive noise are required to be retrieve...
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A novel method for feature extraction based on T-statistic criterion is put forward and introduced for P300 potential detection in Brain-computer interface (BCI) applications. After decorrelation by principal componen...
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Face data has high correlation and redundant information. Discrete cosine transform (DCT) can be efficiently used for redundant data reduction. Primary DCT terms have the most important parameters about the image. For...
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Whilst for the majority of applicationsimage quality depends on sensor accuracy and principles of image formation, in remote sensing systems information is also degraded by communication errors. To improve image fusi...
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ISBN:
(纸本)0819458023
Whilst for the majority of applicationsimage quality depends on sensor accuracy and principles of image formation, in remote sensing systems information is also degraded by communication errors. To improve image fusion results in the presence of communication and sensor impairments we propose a two-stage approach. Preliminary nonlinear locally-adaptive imageprocessing is applied at the first stage for mitigating impairments produced in image sensors and communication systems, and fusion algorithms are used at the second stage. The efficiency of the proposed algorithms is demonstrated for satellite remote sensing images and simulated data with similar characteristics and distortions. The influence of image distortions and the effectiveness of mitigation are estimated for an image fusion architecture for low-level image classification based on artificialneuralnetworks. Experimental results are presented providing quantitative assessment of the proposed algorithms.
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