As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on...
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ISBN:
(纸本)9781424469208
As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on the image contents of the front the camera setting under the rear-view mirror in the vehicle. In this paper, we present a functional-link-based neuro-fuzzy network (FLNFN) structure for lane detection and departure warning system application. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. The lane detection method and the departure warning system proposed in this paper have been successfully evaluated on a PC platform of 3.2-GHz CPU, where the average frame-rate is up to 30fps.
This paper considers online classification learningalgorithms for regularized classification schemes with generalized gradient. A novel capacity independent approach is presented. It verifies the strong convergence o...
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This paper considers online classification learningalgorithms for regularized classification schemes with generalized gradient. A novel capacity independent approach is presented. It verifies the strong convergence of sizes and yields satisfactory convergence rates for polynomially decaying step sizes. Compared with the gradient schemes, this al- gorithm needs only less additional assumptions on the loss function and derives a stronger result with respect to the choice of step sizes and the regularization parameters.
In this paper, we are interested in the analysis of regularized onlinealgorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergen...
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In this paper, we are interested in the analysis of regularized onlinealgorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergence. Explicit learning rates are also given for particular step sizes.
An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of dau...
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An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals.
A new on-line learningalgorithm is derived for blind separation of mixed signals with symmetric distributions. The stability of the method is analysed and compared with the natural gradient method. It is proved that ...
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A new on-line learningalgorithm is derived for blind separation of mixed signals with symmetric distributions. The stability of the method is analysed and compared with the natural gradient method. It is proved that the set of stability conditions obtained is less stringent. Some experiments are included.
On-line learningalgorithms for artificial neural networks (ANNs) are expected to adapt network parameters in order to face new control situations. A new on-line learningalgorithm, based on sliding mode control (SMC)...
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On-line learningalgorithms for artificial neural networks (ANNs) are expected to adapt network parameters in order to face new control situations. A new on-line learningalgorithm, based on sliding mode control (SMC) is presented. The results show that ANN inherits some of the advantages of SMC: high speed of learning and robustness.
In this paper, a constructive algorithm for a general recurrent neural network is proposed based on radial basis functions. It is shown that this algorithm is globally convergent. In addition, we will present two exam...
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In this paper, a constructive algorithm for a general recurrent neural network is proposed based on radial basis functions. It is shown that this algorithm is globally convergent. In addition, we will present two examples to illustrate the proposed method.
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