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21.
Ying Tan 《The Journal of VLSI Signal Processing》2002,32(1-2):45-54
In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications. 相似文献
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A mixed mode digital/analog special purpose VLSI hardware implementation of an associative memory with neural architecture is presented. The memory concept is based on a matrix architecture with binary storage elements holding the connection weights. To enhance the processing speed analog circuit techniques are applied to implement the algorithm for the association. To keep the memory density as high as possible two design strategies are considered. First, the number of transistors per storage element is kept to a minimum. In this paper a circuit technique that uses a single 6-transistor cell for weight storage and analog signal processing is proposed. Second, the device precision has been chosen to a moderate level to save area as much as possible. Since device mismatch limits the performance of analog circuits, the impact of device precision on the circuit performance is explicitly discussed. It is shown that the device precision limits the number of rows activated in parallel. Since the input vector as well as the output vector are considered to be sparsely coded it is concluded, that even for large matrices the proposed circuit technique is appropriate and ultra large scale integration with a large number of connection weights is feasible. 相似文献
24.
E.A. Soliman A.K. Abdelmageed M.A. El-Gamal 《AEUE-International Journal of Electronics and Communications》2002,56(3)
In this paper a new artificial neural network (ANN) based model for the calculation of the method of moments (MoM) matrix elements is presented. Training sets that characterize the matrix elements are first constructed. These sets are then utilized to effectively train two radial basis function (RBF) neural networks to accurately estimate all the elements of the MoM matrix for any mesh used. The potential of the proposed approach is demonstrated in the case of a narrow microstrip line. The current distribution on the microstrip line produced by the trained RBF networks agrees very well with the exact distribution. In addition, the proposed ANN model is much faster than the conventional MoM procedure. 相似文献
25.
Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at 相似文献
26.
WU Jian-Jun GAO Zi-You SUN Hui-Jun 《理论物理通讯》2006,46(7)
In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given. 相似文献
27.
G. Tiana M.H. Jensen K. Sneppen 《The European Physical Journal B - Condensed Matter and Complex Systems》2002,29(1):135-140
A feedback mechanism that involves the proteins p53 and mdm2, induces cell death as a controlled response to severe DNA damage.
A minimal model for this mechanism demonstrates that the response may be dynamic and connected with the time needed to translate
the mdm2 protein. The response takes place if the dissociation constant k between p53 and mdm2 varies from its normal value. Although it is widely believed that it is an increase in k that triggers the response, we show that the experimental behaviour is better described by a decrease in the dissociation
constant. The response is quite robust upon changes in the parameters of the system, as required by any control mechanism,
except for few weak points, which could be connected with the onset of cancer.
Received 8 May 2002 / Received in final form 9 July 2002 Published online 17 September 2002 相似文献
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Arnon Arazi Eshel Ben-Jacob Uri Yechiali 《Mathematical Methods of Operations Research》2005,62(3):453-466
We consider a Jackson-type network comprised of two queues having state-dependent service rates, in which the queue lengths
evolve periodically, exhibiting noisy cycles. To reduce this noise a certain heuristic, utilizing regions in the phase space
in which the system behaves almost deterministically, is applied. Using this heuristic, we show that in order to decrease
the probability of a customers overflow in one of the queues in the network, the server in that same queue – contrary to intuition
– should be shut down for a short period of time. Further noise reduction is obtained if the server in the second queue is
briefly shut down as well, when certain conditions hold. 相似文献