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The aim of this paper is to develop the detection system for present fast-developing LED (light emitted diode) photoelectric industry. To cope with the processing size of next generation dwindles, the gear of the inspection equipment in ultra precision and fast inspection specifications will become one of the important research keys. This paper presents a machine vision to inspect LED microstructure...
This paper presented a new scheme called penalty OBS (optimal brain surgeon) for the feedforward neural network learning. The penalty OBS scheme takes OBS pruning case as a penalty item of the network cost function, and develops two applied methods based on the common algorithms of network learning. As a novel revision of OBS, the new scheme not only saves the runtime to calculate Hessian matrix after...
In this paper, we systematically investigate the long-term, online, real-time variable-bit-rate (VBR) video traffic prediction, which is the key and complicated component for advanced predictive dynamic bandwidth control and allocation framework for the future networks and Internet multimedia services. We focus on neural network-based approach for traffic prediction and demonstrate that the prediction...
The paper proposes a method to discover effectively users' concept patterns when multiple objects of interest (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates multiple instance learning into the user relevance feedback in a seamless way to discover where the user's objects/regions of most interest are and how to map the local...
This research used neural networks to develop a decision support system, and model the relationship between one's living environment and residential satisfaction. Residential satisfaction was investigated at two affordable housing multifamily rental properties located in Atlanta, Georgia. The neural network was trained using data from Defoors Ferry Manor and the network was validated using data from...
We propose three new techniques for training of multilayer neural networks. Its basic concept is based on the gradient descent method. For every methodic are showed formulas for calculation of the adaptive training steps. Matrix algorithmization for all of this techniques are very helpful in its program realization
The computational neural-network structures described in the literature are often based on the concept of linear neural units (LNUs). The biological neuron is a complex computing element, which performs more computations than just linear summation. The computational efficiency of the neural network depends on its structure and the training methods employed. Higher-order combinations of inputs and...
The authors present some preliminary studies in the field of on-orbit modeling and intelligent control technique for flexible space structures using the neutral network approach. The discussions include the state of the art in three areas: (i) on-orbit learning modeling of flexible dynamics using neutral networks, (ii) neutral learning and a control scheme for flexible space structures, and (iii)...
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